Introduction to Artificial Intteligence
Download as PPTX, PDF0 likes124 views
ARTIFICIAL INTELLIGENCE : Introduction , Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment. 2. Problem Solving and Searching Techniques Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A* algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing, Min-Max and Alpha-Beta pruning algorithms. 3. Knowledge Representation Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets, Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs. Programming in Logic (PROLOG) 4. Dealing with Uncertainty and Inconsistencies Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic Inference, Possible World Representations. 5. Understanding Natural Languages Parsing Techniques, Context-Free and Transformational Grammars, Recursive and Augmented Transition Nets.
1 of 17
Download to read offline
Ad
Recommended
Artificial Intelligence
Artificial IntelligenceSyed Zaid Irshad This document provides an overview of an introductory course on artificial intelligence. It discusses four views of AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. The textbook advocates the view of "acting rationally" by designing agents that maximize goal achievement given available information. A brief history of AI is also provided, from early work in philosophy, mathematics, and the sciences to landmark developments like Turing's 1950 paper posing the question "Can machines think?". The state of the art in AI is summarized with examples like Deep Blue defeating Kasparov at chess in 1997 and autonomous vehicles driving 98% of the time across the US.
Finite automata-for-lexical-analysis
Finite automata-for-lexical-analysisDattatray Gandhmal recognizer for a language, Deterministic finite automata, Non-deterministic finite automata, conversion of NFA to DFA, Regular Expression to NFA, Thomsons Construction
NFA & DFA
NFA & DFAAkhil Kaushik It explains the process of Non deterministic Finite Automata and Deterministic Finite Automata. It also explains how to convert NFA to DFA.
Problem solving agents
Problem solving agentsMegha Sharma This document discusses problem solving agents in artificial intelligence. It explains that problem solving agents focus on satisfying goals by formulating the goal based on the current situation, then formulating the problem by determining the actions needed to achieve the goal. Key components of problem formulation include the initial state, possible actions, transition model describing how actions change the state, a goal test, and path cost function. Two examples of well-defined problems are given: the 8-puzzle problem and the 8-queens problem.
NFA or Non deterministic finite automata
NFA or Non deterministic finite automatadeepinderbedi An NFA (non-deterministic finite automaton) can have multiple transitions from a single state on a given input symbol, whereas a DFA (deterministic finite automaton) has exactly one transition from each state on each symbol. The document discusses NFAs and how they differ from DFAs, provides examples of NFA diagrams, and describes how to convert an NFA to an equivalent DFA.
Introduction to fa and dfa
Introduction to fa and dfadeepinderbedi 1. Automata theory is the study of abstract machines and the problems they are able to solve. It is closely related to formal language theory as automata are often classified by the formal languages they can recognize.
2. A finite automaton is an abstract machine that consists of a finite number of states. It reads an input string and based on its current state and the next input symbol, transitions to the next state according to its transition function. If it ends in an accepting state, the input is accepted.
3. Deterministic finite automata (DFAs) are a type of finite automaton where the transition function maps each state-symbol pair to a unique next state. DFAs can be represented
AI for Everyone: Master the Basics
AI for Everyone: Master the BasicsStutty Srivastava The document provides an overview of artificial intelligence (AI) concepts and applications through a 4-module online course. Module 1 defines AI and common applications like healthcare, education, and customer service. Module 2 covers machine learning, deep learning, neural networks, and their various applications. Module 3 discusses issues around AI including privacy, job disruption, bias, and ethics. Module 4 explores the future of AI and how to start a career in the field.
1.2. introduction to automata theory
1.2. introduction to automata theorySampath Kumar S Introduction to automata theory. What is automata theory? why it is named so? History of Automata theory
The structure of agents
The structure of agentsAnitha Purushothaman The document discusses different types of agent structures and programs. It describes simple reflex agents that select actions based only on the current percept. Model based reflex agents maintain an internal state based on the percept history. Goal-based agents choose actions that achieve defined goals, while utility based agents aim to maximize utility or happiness across states. Learning agents can improve by making modifications based on feedback from experiences.
Predicates
PredicatesBindhuBhargaviTalasi The document discusses several key concepts in predicate logic including:
- Predicates and how statements like "John is a politician" can be represented symbolically as P(j)
- Universal and existential quantifiers and how they are used to represent statements like "all men are mortal" and "some lions are dangerous"
- The difference between free and bound variables in quantified statements
- Rules of inference for quantified statements like universal specification, existential generalization, etc.
- How to symbolize arguments and prove their validity using rules of inference
Several examples are provided to illustrate how to symbolize arguments using predicates and quantifiers and how to formally prove them.
Informed and Uninformed search Strategies
Informed and Uninformed search StrategiesAmey Kerkar 1. The document discusses various search strategies used to solve problems including uninformed search strategies like breadth-first search and depth-first search, and informed search strategies like best-first search and A* search that use heuristics.
2. It provides examples and explanations of breadth-first search, depth-first search, hill climbing, and best-first search algorithms. Key advantages and disadvantages of each strategy are outlined.
3. The document focuses on explaining control strategies for problem solving, different types of search strategies classified as uninformed or informed, and algorithms for breadth-first search, depth-first search, hill climbing, and best-first search.
VTU Design and Analysis of Algorithms(DAA) Lab Manual by Nithin, VVCE, Mysuru...
VTU Design and Analysis of Algorithms(DAA) Lab Manual by Nithin, VVCE, Mysuru...Nithin Kumar,VVCE, Mysuru VTU Fourth Semester Design and Analysis of Algorithms Laboratory Manual As per CBCS Scheme by Nithin Kumar, VVCE, Mysuru
Heuristic Search Techniques Unit -II.ppt
Heuristic Search Techniques Unit -II.pptkarthikaparthasarath This document discusses various heuristic search techniques used in artificial intelligence. It begins by defining heuristics as techniques that find approximate solutions faster than classic methods when exact solutions are not possible or not feasible due to time or memory constraints. It then describes heuristic search, hill climbing, simulated annealing, A* search, and best-first search. Hill climbing is presented as an example heuristic technique that evaluates neighboring states to move toward an optimal solution. The document also discusses problems that can occur with hill climbing like getting stuck in local maxima.
Artificial Neural Networks (ANNs) - XOR - Step-By-Step
Artificial Neural Networks (ANNs) - XOR - Step-By-StepAhmed Gad Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Moore and mealy machines
Moore and mealy machinesAYESHA JAVED Moore and Mealy machines are two types of finite state machines. A Mealy machine's output depends on the current state and input, and its output size equals its input size. A Moore machine's output depends only on the current state, and its output size is one larger than its input size. Mealy machines are defined as tuples including states, inputs, outputs, transitions, and an output function. Moore machines are similarly defined except the output function maps states to outputs rather than states and inputs. Examples of Moore and Mealy machine applications include elevators, compilers, SRAM, and vending machines.
Scala Talk at FOSDEM 2009
Scala Talk at FOSDEM 2009Martin Odersky The document discusses Scala, a programming language designed to be scalable. It can be used for both small and large programs. Scala combines object-oriented and functional programming. It interoperates seamlessly with Java but allows developers to add new constructs like actors through libraries. The Scala community is growing, with industrial adoption starting at companies like Twitter.
Deep Feed Forward Neural Networks and Regularization
Deep Feed Forward Neural Networks and RegularizationYan Xu Deep feedforward networks use regularization techniques like L2/L1 regularization, dropout, batch normalization, and early stopping to reduce overfitting. They employ techniques like data augmentation to increase the size and variability of training datasets. Backpropagation allows information about the loss to flow backward through the network to efficiently compute gradients and update weights with gradient descent.
Functional Programming 101 with Scala and ZIO @FunctionalWorld
Functional Programming 101 with Scala and ZIO @FunctionalWorldJorge Vásquez This document provides an overview of functional programming concepts and the ZIO library. It discusses the characteristics of pure functions, including being total, deterministic, and having no side effects. It contrasts functional programming with object-oriented programming. Benefits of the functional paradigm discussed include local reasoning, referential transparency, conciseness, easier testing, and support for parallel programming. The document then introduces the ZIO library, describing how it allows building resilient, asynchronous, and efficient applications using functional principles. It outlines ZIO's data type and how it represents effects.
Automated Python Test Frameworks for Hardware Verification and Validation
Automated Python Test Frameworks for Hardware Verification and ValidationBarbara Jones This document discusses automated testing of hardware using Python test frameworks. It describes using test frameworks to design broad hardware tests from specifications, control external equipment, and handle test data analysis. As an example, it outlines a phase alignment test between multiple instruments, including setting up the test with a waveform generator, running a sequence of configurations, and calculating the discrete Fourier transform of the results to check for phase differences within a tolerance. The goal is to validate and verify hardware using an automated and flexible test framework approach.
Regular expression
Regular expressionRajon The document discusses regular expressions (regex), including what they are, common operations used in regex like concatenation and Kleene closure, how to convert regular expressions to nondeterministic finite automata (NFA), the differences between deterministic and nondeterministic finite automata, and some examples of using regex in programming and tools.
Hardware Implementation of Spiking Neural Network (SNN)
Hardware Implementation of Spiking Neural Network (SNN)supratikmondal6 This project work was carried out under the supervision of Dr. Gaurav Trivedi (IIT Guwahati, Electrical Engineering) and under the mentorship of Mr. Ashvinikumar Pruthviraj Dongre (IIT Guwahati, PhD Scholar). In this project we have tried to implement the SNN for image classification in FPGA by
developing an efficient and realistic architecture and also by incorporating a technique of weight change according to
Step-Wise STDP learning curve.
Operating system and C++ paper for CCEE
Operating system and C++ paper for CCEEprabhatjon this is paper for c++ and Operating system for CCEE (CDAC final examination)
computer notes - Traversal of a binary tree
computer notes - Traversal of a binary treeecomputernotes This document discusses level-order traversal of a binary tree using a queue. It explains that level-order traversal prints the nodes level by level from left to right. A queue is used to keep track of nodes to visit at each level. The root is first added to the queue, then nodes are dequeued and their children added until the queue is empty, printing the value of each node. Pseudocode and an example are provided to illustrate how level-order traversal works by traversing the levels of the tree from top to bottom.
AI - Intelligent Agents
AI - Intelligent AgentsAndrew Ferlitsch This document discusses four types of learning agents in artificial intelligence: simple reflex agents, model-based agents, goal-based agents, and utility-based agents. A learning agent improves upon these by dynamically learning a policy to model its environment and build action-state rules based on rewards from interacting with its environment.
Lecture: Automata
Lecture: AutomataMarina Santini The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
Ai lecture 01(unit03)
Ai lecture 01(unit03)vikas dhakane Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Question Answering System using machine learning approach
Question Answering System using machine learning approachGarima Nanda In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
Feedforward neural network
Feedforward neural networkSopheaktra YONG This slide is prepared for the lectures-in-turn challenge within the study group of social informatics, kyoto university.
Artificial Intelligence
Artificial IntelligencePatrick Walter Artificial intelligence (AI) is the ability of machines to mimic human intelligence through tasks like thinking, reasoning, and learning. The document discusses the history and current applications of AI as well as its future potential. Current applications of AI include finance/banking, healthcare, transportation, and consumer devices. Advancements in natural language processing, computer vision, machine learning, and robotics may allow AI to achieve human-level abilities and be applied to problems like risk management and disaster response. However, as AI becomes more advanced, it also raises social and ethical implications around robot rights, personhood, and what constitutes a moral agent.
NCompass Live: AI: The Modern Day Pandora's Box
NCompass Live: AI: The Modern Day Pandora's BoxBrian Pichman Artificial intelligence (AI) has brought many opportunities but also challenges and ethical concerns, similar to Pandora's box. The document discusses AI applications and implications, exploring how AI can be used as a force for good or fall short. It also examines industries adopting AI and strategies for responsible integration of AI. The key topics are the multifaceted nature of AI, ethical questions around AI, and how AI can both help and potentially harm individuals and society.
Ad
More Related Content
What's hot (20)
The structure of agents
The structure of agentsAnitha Purushothaman The document discusses different types of agent structures and programs. It describes simple reflex agents that select actions based only on the current percept. Model based reflex agents maintain an internal state based on the percept history. Goal-based agents choose actions that achieve defined goals, while utility based agents aim to maximize utility or happiness across states. Learning agents can improve by making modifications based on feedback from experiences.
Predicates
PredicatesBindhuBhargaviTalasi The document discusses several key concepts in predicate logic including:
- Predicates and how statements like "John is a politician" can be represented symbolically as P(j)
- Universal and existential quantifiers and how they are used to represent statements like "all men are mortal" and "some lions are dangerous"
- The difference between free and bound variables in quantified statements
- Rules of inference for quantified statements like universal specification, existential generalization, etc.
- How to symbolize arguments and prove their validity using rules of inference
Several examples are provided to illustrate how to symbolize arguments using predicates and quantifiers and how to formally prove them.
Informed and Uninformed search Strategies
Informed and Uninformed search StrategiesAmey Kerkar 1. The document discusses various search strategies used to solve problems including uninformed search strategies like breadth-first search and depth-first search, and informed search strategies like best-first search and A* search that use heuristics.
2. It provides examples and explanations of breadth-first search, depth-first search, hill climbing, and best-first search algorithms. Key advantages and disadvantages of each strategy are outlined.
3. The document focuses on explaining control strategies for problem solving, different types of search strategies classified as uninformed or informed, and algorithms for breadth-first search, depth-first search, hill climbing, and best-first search.
VTU Design and Analysis of Algorithms(DAA) Lab Manual by Nithin, VVCE, Mysuru...
VTU Design and Analysis of Algorithms(DAA) Lab Manual by Nithin, VVCE, Mysuru...Nithin Kumar,VVCE, Mysuru VTU Fourth Semester Design and Analysis of Algorithms Laboratory Manual As per CBCS Scheme by Nithin Kumar, VVCE, Mysuru
Heuristic Search Techniques Unit -II.ppt
Heuristic Search Techniques Unit -II.pptkarthikaparthasarath This document discusses various heuristic search techniques used in artificial intelligence. It begins by defining heuristics as techniques that find approximate solutions faster than classic methods when exact solutions are not possible or not feasible due to time or memory constraints. It then describes heuristic search, hill climbing, simulated annealing, A* search, and best-first search. Hill climbing is presented as an example heuristic technique that evaluates neighboring states to move toward an optimal solution. The document also discusses problems that can occur with hill climbing like getting stuck in local maxima.
Artificial Neural Networks (ANNs) - XOR - Step-By-Step
Artificial Neural Networks (ANNs) - XOR - Step-By-StepAhmed Gad Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Moore and mealy machines
Moore and mealy machinesAYESHA JAVED Moore and Mealy machines are two types of finite state machines. A Mealy machine's output depends on the current state and input, and its output size equals its input size. A Moore machine's output depends only on the current state, and its output size is one larger than its input size. Mealy machines are defined as tuples including states, inputs, outputs, transitions, and an output function. Moore machines are similarly defined except the output function maps states to outputs rather than states and inputs. Examples of Moore and Mealy machine applications include elevators, compilers, SRAM, and vending machines.
Scala Talk at FOSDEM 2009
Scala Talk at FOSDEM 2009Martin Odersky The document discusses Scala, a programming language designed to be scalable. It can be used for both small and large programs. Scala combines object-oriented and functional programming. It interoperates seamlessly with Java but allows developers to add new constructs like actors through libraries. The Scala community is growing, with industrial adoption starting at companies like Twitter.
Deep Feed Forward Neural Networks and Regularization
Deep Feed Forward Neural Networks and RegularizationYan Xu Deep feedforward networks use regularization techniques like L2/L1 regularization, dropout, batch normalization, and early stopping to reduce overfitting. They employ techniques like data augmentation to increase the size and variability of training datasets. Backpropagation allows information about the loss to flow backward through the network to efficiently compute gradients and update weights with gradient descent.
Functional Programming 101 with Scala and ZIO @FunctionalWorld
Functional Programming 101 with Scala and ZIO @FunctionalWorldJorge Vásquez This document provides an overview of functional programming concepts and the ZIO library. It discusses the characteristics of pure functions, including being total, deterministic, and having no side effects. It contrasts functional programming with object-oriented programming. Benefits of the functional paradigm discussed include local reasoning, referential transparency, conciseness, easier testing, and support for parallel programming. The document then introduces the ZIO library, describing how it allows building resilient, asynchronous, and efficient applications using functional principles. It outlines ZIO's data type and how it represents effects.
Automated Python Test Frameworks for Hardware Verification and Validation
Automated Python Test Frameworks for Hardware Verification and ValidationBarbara Jones This document discusses automated testing of hardware using Python test frameworks. It describes using test frameworks to design broad hardware tests from specifications, control external equipment, and handle test data analysis. As an example, it outlines a phase alignment test between multiple instruments, including setting up the test with a waveform generator, running a sequence of configurations, and calculating the discrete Fourier transform of the results to check for phase differences within a tolerance. The goal is to validate and verify hardware using an automated and flexible test framework approach.
Regular expression
Regular expressionRajon The document discusses regular expressions (regex), including what they are, common operations used in regex like concatenation and Kleene closure, how to convert regular expressions to nondeterministic finite automata (NFA), the differences between deterministic and nondeterministic finite automata, and some examples of using regex in programming and tools.
Hardware Implementation of Spiking Neural Network (SNN)
Hardware Implementation of Spiking Neural Network (SNN)supratikmondal6 This project work was carried out under the supervision of Dr. Gaurav Trivedi (IIT Guwahati, Electrical Engineering) and under the mentorship of Mr. Ashvinikumar Pruthviraj Dongre (IIT Guwahati, PhD Scholar). In this project we have tried to implement the SNN for image classification in FPGA by
developing an efficient and realistic architecture and also by incorporating a technique of weight change according to
Step-Wise STDP learning curve.
Operating system and C++ paper for CCEE
Operating system and C++ paper for CCEEprabhatjon this is paper for c++ and Operating system for CCEE (CDAC final examination)
computer notes - Traversal of a binary tree
computer notes - Traversal of a binary treeecomputernotes This document discusses level-order traversal of a binary tree using a queue. It explains that level-order traversal prints the nodes level by level from left to right. A queue is used to keep track of nodes to visit at each level. The root is first added to the queue, then nodes are dequeued and their children added until the queue is empty, printing the value of each node. Pseudocode and an example are provided to illustrate how level-order traversal works by traversing the levels of the tree from top to bottom.
AI - Intelligent Agents
AI - Intelligent AgentsAndrew Ferlitsch This document discusses four types of learning agents in artificial intelligence: simple reflex agents, model-based agents, goal-based agents, and utility-based agents. A learning agent improves upon these by dynamically learning a policy to model its environment and build action-state rules based on rewards from interacting with its environment.
Lecture: Automata
Lecture: AutomataMarina Santini The document provides an introduction to automata theory and finite state automata (FSA). It defines an automaton as an abstract computing device or mathematical model used in computer science and computational linguistics. The reading discusses pioneers in automata theory like Alan Turing and his development of Turing machines. It then gives an overview of finite state automata, explaining concepts like states, transitions, alphabets, and using a example of building an FSA for a "sheeptalk" language to demonstrate these components.
Ai lecture 01(unit03)
Ai lecture 01(unit03)vikas dhakane Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
Question Answering System using machine learning approach
Question Answering System using machine learning approachGarima Nanda In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
Feedforward neural network
Feedforward neural networkSopheaktra YONG This slide is prepared for the lectures-in-turn challenge within the study group of social informatics, kyoto university.
VTU Design and Analysis of Algorithms(DAA) Lab Manual by Nithin, VVCE, Mysuru...
VTU Design and Analysis of Algorithms(DAA) Lab Manual by Nithin, VVCE, Mysuru...Nithin Kumar,VVCE, Mysuru
Similar to Introduction to Artificial Intteligence (20)
Artificial Intelligence
Artificial IntelligencePatrick Walter Artificial intelligence (AI) is the ability of machines to mimic human intelligence through tasks like thinking, reasoning, and learning. The document discusses the history and current applications of AI as well as its future potential. Current applications of AI include finance/banking, healthcare, transportation, and consumer devices. Advancements in natural language processing, computer vision, machine learning, and robotics may allow AI to achieve human-level abilities and be applied to problems like risk management and disaster response. However, as AI becomes more advanced, it also raises social and ethical implications around robot rights, personhood, and what constitutes a moral agent.
NCompass Live: AI: The Modern Day Pandora's Box
NCompass Live: AI: The Modern Day Pandora's BoxBrian Pichman Artificial intelligence (AI) has brought many opportunities but also challenges and ethical concerns, similar to Pandora's box. The document discusses AI applications and implications, exploring how AI can be used as a force for good or fall short. It also examines industries adopting AI and strategies for responsible integration of AI. The key topics are the multifaceted nature of AI, ethical questions around AI, and how AI can both help and potentially harm individuals and society.
ARTIFICIAL INTELLIGENCE 
ARTIFICIAL INTELLIGENCE Bharat Bhushan ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
ARTIFICIAL INTELLIGENCE SYLLABUS BCA (1).pdf
ARTIFICIAL INTELLIGENCE SYLLABUS BCA (1).pdfBharat Bhushan @ B K NAL ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
ARTIFICIAL INTELLIGENCE 
ARTIFICIAL INTELLIGENCE Bharat Bhushan @ B K NAL This document outlines the presentation topics for an artificial intelligence course given by Bharat Bhushan, an assistant professor at RLSY College in Ranchi, India. The presentation covers 5 units: an introduction to AI, problem solving and searching techniques, knowledge representation, and dealing with uncertainty. Some of the specific techniques and topics that will be discussed include the Turing test, intelligent agents, search algorithms like A* and alpha-beta pruning, knowledge representation schemes like semantic nets and frames, probabilistic reasoning, and parsing techniques.
ARTIFICIAL INTELLIGENCE SYLLABUS 
ARTIFICIAL INTELLIGENCE SYLLABUS Bharat Bhushan ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
AI for Beginners - SWFLN Makerpalooza - Session 1
AI for Beginners - SWFLN Makerpalooza - Session 1Brian Pichman This session explores what artificial intelligence (AI) is and the everyday use cases for AI. It’s an introductory look at how various industries, including libraries, use AI for operational efficiencies, enhanced services, and more.
Getting Started With Using AI In Libraries (PLAN)
Getting Started With Using AI In Libraries (PLAN)Brian Pichman This document provides an introduction to artificial intelligence (AI) concepts. It discusses what AI and machine learning are, common applications of AI today, and considerations around its development and use. The document aims to explain AI in an accessible way for newcomers to understand its potential impacts and how it is already integrated into many technologies and industries.
IRJET- Relative Study on Artificial Intelligence Interaction with Human Emotions
IRJET- Relative Study on Artificial Intelligence Interaction with Human EmotionsIRJET Journal The document discusses artificial intelligence and its interaction with human emotions. It provides background on AI, including definitions, history, and current applications. It explores how AI is beginning to understand human emotions through voice recognition, facial expression analysis, and other methods. It also examines scenarios where AI could potentially become dangerous if not properly programmed or regulated, such as if autonomous weapons were not kept under human control. The document concludes that AI is making progress in detecting human emotions and will likely become more integrated into daily life.
Artificial Intelligence
Artificial Intelligenceguest45deb89 This document provides a history and overview of artificial intelligence (AI). It discusses early pioneers in AI such as Alan Turing and his introduction of the Turing Test. Key areas of AI research and applications are then summarized, including intelligent robotics, automated reasoning, authorizing financial transactions, diagnosing medical problems, and computer vision. The document also briefly outlines fields within AI like intelligent agents and pattern recognition. In closing, it emphasizes how AI is being used to configure hardware/software, schedule manufacturing, and process large amounts of data efficiently.
What Is Artificial Intelligence,How It Is Used and Its Future.pdf
What Is Artificial Intelligence,How It Is Used and Its Future.pdfMaazUmar3 What Is Artificial Intelligence,How It Is Used in now days and what is Its Future that help the humans also contain full history of artificial intelligence.
Onboarding AI & Machine Learning
Onboarding AI & Machine LearningBrian Pichman The document discusses onboarding artificial intelligence and machine learning. It begins by outlining some key questions to consider when introducing advanced technology like AI into an organization, such as how to introduce it to staff and address concerns about jobs being replaced. It then provides definitions and examples of artificial intelligence and machine learning. The rest of the document covers topics like how AI is used in everyday life via applications like voice assistants and image recognition. It also discusses challenges of AI and ways libraries can embrace and apply AI concepts.
Artificial intelligence-part-iii
Artificial intelligence-part-iiiTONI TRIYULIANTO The document discusses The IIA's Artificial Intelligence Auditing Framework for internal auditors. The framework addresses AI strategy, governance, and the human factor. It includes seven elements: cyber resilience, AI competencies, data quality, data architecture/infrastructure, measuring performance, ethics, and the black box. The human factor component deals with risks of human error and biases affecting AI results. It is important to identify and manage biases, ensure AI is tested and outputs are used legally and ethically. The black box element refers to hidden internal mechanisms of advanced AI becoming less transparent. Governance establishes accountability over AI activities and skills. Measuring performance involves advising on AI metrics and providing assurance over controls related to AI initiatives.
aman presentation 2.pptx
aman presentation 2.pptxSanuBose it is for AI and ML a short ppt notes i hope it is useful for yours will study and increase your scores
Introduction to Artificial Intelligence
Introduction to Artificial IntelligenceKalai Selvi This lecture slide introduces the basics of AI ,its applications, comparison with conventional algorithms
Introduction to Artificial Intelligence
Introduction to Artificial IntelligenceKalai Selvi The document discusses artificial intelligence (AI) and defines it as developing computer programs that can solve complex problems using processes analogous to human reasoning. It describes three aspects of AI programming: learning, reasoning, and self-correction. An example is given of using large amounts of historical data to train a machine learning model to predict weather forecasts. The goals of AI are also outlined, such as creating expert systems, implementing human intelligence in machines, and developing intelligent robots.
DEBS 2015 tutorial When Artificial Intelligence meets the Internet of Things
DEBS 2015 tutorial When Artificial Intelligence meets the Internet of ThingsOpher Etzion Siri, Alexa, Cortana
2017
Actuators: Speech, screen
2018
WHAT IS ARTIFICIAL INTELLIGENCE IN SIMPLE WORDS.pdf
WHAT IS ARTIFICIAL INTELLIGENCE IN SIMPLE WORDS.pdfSyedZakirHussian Artificial Intelligence is a term that refers to the creation of computer systems with the ability to think and act intelligently. Many believe that with the advancement of technology, artificial intelligence is quickly becoming a reality. In this sense, the future is already here- and it’s just as terrifying as we thought it would be.
In 1943, Alan Turing published a paper titled On Computing Machinery, later referred to as the Turing Test. In this paper, Turing proposed that a computer could be considered to be as intelligent as a human being if it could pass a test designed to simulate common human behaviors. When testing a computer’s intelligence, many consider self-awareness to be an important factor. Essentially, a system must be able to recognize its own emotions and behaviors and respond to them appropriately.
Definition Of Artificial Intelligence
AI is commonly defined as a branch of study that focuses on building intelligent machines. However, there is no standard definition of AI. Therefore, there are many different approaches to creating intelligent machines. Some consider artificial intelligence to be an extension of human cognition; others believe that it’s an entirely new type of intelligence. Some consider artificial intelligence to be a threat while others believe it can help mankind advance in many ways.
Use Of Aritificial Inteligence
AI has been developed and applied in many different areas- including military, medical, and entertainment systems. The field of AI is rapidly growing in both popularity and complexity. Thanks to technological advancement, we no longer need to manually control complex machines with laborious human operators. Instead, we can program our robotic controls with advanced algorithms using artificial intelligence. We can also train our AI systems using machine learning so that they become more effective over time.
Unit 1 part 1
Unit 1 part 1Kalai Selvi Artificial intelligence (AI) focuses on learning, reasoning, and self-correction processes to mimic human cognition. It works by feeding large amounts of data into algorithms that learn patterns to predict outcomes. The goals of AI include creating expert systems that exhibit intelligent behavior and implementing human intelligence in machines to perform complex tasks like driving cars. Advantages of AI include using robots like Sophia for healthcare, solving crimes, education, and business. However, disadvantages are that AI may replace jobs and make people lazy.
Ad
More from Bharat Bhushan (20)
NIMCET 2023 : Questions
NIMCET 2023 : QuestionsBharat Bhushan This document contains a test examination with multiple choice questions on mathematics, English, computer science, and reasoning. There are 52 questions in total covering topics like trigonometry, sets, relations, sequences, coding/decoding, direction sense, data interpretation from tables, and basic computer concepts. The test is for a batch of NIMCET conducted in March 2022 by Buddha Science and Technical Institute located in Kokar, Ranchi.
Heuristics Search Techniques in AI 
Heuristics Search Techniques in AI Bharat Bhushan What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Heuristics Search Techniques
Heuristics Search TechniquesBharat Bhushan What is Heuristics?
A heuristic is a technique that is used to solve a problem faster than the classic methods. These techniques are used to find the approximate solution of a problem when classical methods do not. Heuristics are said to be the problem-solving techniques that result in practical and quick solutions.
Heuristics are strategies that are derived from past experience with similar problems. Heuristics use practical methods and shortcuts used to produce the solutions that may or may not be optimal, but those solutions are sufficient in a given limited timeframe.
History
Psychologists Daniel Kahneman and Amos Tversky have developed the study of Heuristics in human decision-making in the 1970s and 1980s. However, this concept was first introduced by the Nobel Laureate Herbert A. Simon, whose primary object of research was problem-solving.
Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of a short-term solution. On facing complex situations with limited resources and time, Heuristics can help the companies to make quick decisions by shortcuts and approximated calculations. Most of the heuristic methods involve mental shortcuts to make decisions on past experiences.
Heuristic techniques
The heuristic method might not always provide us the finest solution, but it is assured that it helps us find a good solution in a reasonable time.
Based on context, there can be different heuristic methods that correlate with the problem's scope. The most common heuristic methods are - trial and error, guesswork, the process of elimination, historical data analysis. These methods involve simply available information that is not particular to the problem but is most appropriate. They can include representative, affect, and availability heuristics.
We can perform the Heuristic techniques into two categories:
Direct Heuristic Search techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search techniques are not always possible as they require much memory and time. These techniques search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and Depth First Search (DFS).
Weak Heuristic Search techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques are helpful when they are applied properly to the right types of tasks. They usually require domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial IntelligenceBharat Bhushan Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space.
Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.
No backtracking: It does not backtrack the search space, as it does not remember the previous states.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial IntelligenceBharat Bhushan Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space.
Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.
No backtracking: It does not backtrack the search space, as it does not remember the previous states.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
Control Strategies in AI 
Control Strategies in AI Bharat Bhushan Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Control Strategies in AI 
Control Strategies in AI Bharat Bhushan Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Dynamic Programming
Dynamic ProgrammingBharat Bhushan Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Algorithm Design Technique 
Algorithm Design Technique Bharat Bhushan Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Production System in AI
Production System in AIBharat Bhushan Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Production System in AI
Production System in AIBharat Bhushan Production System in Artificial Intelligence (AI)
A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings.
A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems.
Global Database
A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances.
Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now!
Production Rules
Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side.
Control System
The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result.
Simplicity
The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial IntelligenceBharat Bhushan Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial IntelligenceBharat Bhushan Artificial Intelligence is a “way of making a computer, a computer-controlled robot, or software think intelligently, in the similar manner the intelligent humans think”.
Since artificial intelligence (AI) is mainly related to the search process, it is important to have some methodology to choose the best possible solution.
To choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Introduction to Artificial Intteligence
Introduction to Artificial IntteligenceBharat Bhushan ARTIFICIAL INTELLIGENCE : Introduction ,
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and
environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir 
DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir Bharat Bhushan DESIGN AND ANALYSIS OF ALGORITHMS
1. Introduction
Basic Design and Analysis techniques of Algorithms, Correctness of Algorithm.
2. Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
3. Sorting and Searching Techniques
Elementary sorting techniques–Bubble Sort, Insertion Sort, Merge Sort, Advanced Sorting
techniques - Heap Sort, Quick Sort, Sorting in Linear Time - Bucket Sort, Radix Sort and Count
Sort, Searching Techniques, Medians & Order Statistics, complexity analysis;
4. Lower Bounding Techniques
Decision Trees
5. Balanced Trees
Red-Black Trees
6. Advanced Analysis Technique
Amortized analysis
7. Graphs
Graph Algorithms–Breadth First Search, Depth First Search and its Applications, Minimum
Spanning Trees.
DESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMSBharat Bhushan This document outlines the units of a course on design and analysis of algorithms taught by Bharat Bhushan. The course covers basic design techniques like iteration, divide and conquer, and dynamic programming. It also covers sorting algorithms like bubble sort, merge sort, and quick sort. Additionally, the course addresses searching techniques, lower bounding techniques, advanced analysis like amortized analysis, and graph algorithms like breadth first search and minimum spanning trees. The overall document provides an overview of the topics and units to be covered in the algorithms course.
Cyber Security 
Cyber Security Bharat Bhushan Presentation on Cyber Security by Bharat Bhushan, Assistant Professor, Ram Lakhan Singh Yadav College, Kokar, Ranchi.
Cyber Security 
Cyber Security Bharat Bhushan Presentation on Cyber Security by Bharat Bhushan, Assistant Professor, Ram Lakhan Singh Yadav College, Kokar, Ranchi.
Digital Content Creation By Bharat Sir Kokar
Digital Content Creation By Bharat Sir KokarBharat Bhushan रांची, 24 जुलाई : विश्व संवाद केंद्र झारखंड के तत्वाधान में डिजिटल कंटेंट क्रिएशन एंड यूटिलाइजेशन फॉर स्टूडेंट्स एंड इनफार्मेशन प्रोफेशनल विषय पर एक दिवसीय प्रशिक्षण कार्यक्रम का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, कोकर के सभागार में आयोजित किया गया। कार्यक्रम का उद्घाटन इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडे ने दीप प्रज्वलित कर किया ।
इंफॉर्मेशन साइंटिस्ट भारत भूषण ने छात्रों को डिजिटल कंटेंट क्रिएट करने और उसका उपयोग अपने और देश हित में कैसे कर सकते हैं इस विषय पर छात्रों को प्रशिक्षित किया।
कार्यक्रम के समापन में मुख्य अतिथि के तौर पर राम लखन सिंह यादव कॉलेज के प्रिंसिपल डॉ. जे.पी. सिंह ने छात्रों को कहा कि आज का दौर टेक्नोलॉजी और इंफॉर्मेशन का है। आज वही सबसे ज्यादा विकसित राष्ट्र कहलाएगा जिसके पास जितना ज्यादा से ज्यादा विकसित टेक्नोलॉजी होगा। कार्यक्रम के दौरान बेहतर प्रदर्शन करने वाले छात्रों को डॉ. जे.पी. सिंह ने मेडल देकर सम्मानित किया।
कार्यक्रम को सफल बनाने में सुप्रिया भारती, सावित्री कुमारी, हरेंद्र कुमार, निशांत दीप दास, छाया महतो, अंकित रंजन एवं किशोरी शाह का मुख्य योगदान रहा।
Digital Content Creation by Bharat Sir Kokar
Digital Content Creation by Bharat Sir KokarBharat Bhushan रांची, 24 जुलाई : विश्व संवाद केंद्र झारखंड के तत्वाधान में डिजिटल कंटेंट क्रिएशन एंड यूटिलाइजेशन फॉर स्टूडेंट्स एंड इनफार्मेशन प्रोफेशनल विषय पर एक दिवसीय प्रशिक्षण कार्यक्रम का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, कोकर के सभागार में आयोजित किया गया। कार्यक्रम का उद्घाटन इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडे ने दीप प्रज्वलित कर किया ।
इंफॉर्मेशन साइंटिस्ट भारत भूषण ने छात्रों को डिजिटल कंटेंट क्रिएट करने और उसका उपयोग अपने और देश हित में कैसे कर सकते हैं इस विषय पर छात्रों को प्रशिक्षित किया।
कार्यक्रम के समापन में मुख्य अतिथि के तौर पर राम लखन सिंह यादव कॉलेज के प्रिंसिपल डॉ. जे.पी. सिंह ने छात्रों को कहा कि आज का दौर टेक्नोलॉजी और इंफॉर्मेशन का है। आज वही सबसे ज्यादा विकसित राष्ट्र कहलाएगा जिसके पास जितना ज्यादा से ज्यादा विकसित टेक्नोलॉजी होगा। कार्यक्रम के दौरान बेहतर प्रदर्शन करने वाले छात्रों को डॉ. जे.पी. सिंह ने मेडल देकर सम्मानित किया।
कार्यक्रम को सफल बनाने में सुप्रिया भारती, सावित्री कुमारी, हरेंद्र कुमार, निशांत दीप दास, छाया महतो, अंकित रंजन एवं किशोरी शाह का मुख्य योगदान रहा।
Ad
Recently uploaded (20)
Handling Multiple Choice Responses: Fortune Effiong.pptx
Handling Multiple Choice Responses: Fortune Effiong.pptxAuthorAIDNationalRes INTRO TO STATISTICS
INTRO TO SPSS INTERFACE
CLEANING MULTIPLE CHOICE RESPONSE DATA WITH EXCEL
ANALYZING MULTIPLE CHOICE RESPONSE DATA
INTERPRETATION
Q & A SESSION
PRACTICAL HANDS-ON ACTIVITY
Multi-currency in odoo accounting and Update exchange rates automatically in ...
Multi-currency in odoo accounting and Update exchange rates automatically in ...Celine George Most business transactions use the currencies of several countries for financial operations. For global transactions, multi-currency management is essential for enabling international trade.
LDMMIA Reiki Master Spring 2025 Mini Updates
LDMMIA Reiki Master Spring 2025 Mini UpdatesLDM Mia eStudios As of Mid to April Ending, I am building a new Reiki-Yoga Series. No worries, they are free workshops. So far, I have 3 presentations so its a gradual process. If interested visit: https://www.slideshare.net/YogaPrincess
https://ldmchapels.weebly.com
Blessings and Happy Spring. We are hitting Mid Season.
One Hot encoding a revolution in Machine learning
One Hot encoding a revolution in Machine learningmomer9505 A brief introduction to ONE HOT encoding a way to communicate with machines
How to manage Multiple Warehouses for multiple floors in odoo point of sale
How to manage Multiple Warehouses for multiple floors in odoo point of saleCeline George The need for multiple warehouses and effective inventory management is crucial for companies aiming to optimize their operations, enhance customer satisfaction, and maintain a competitive edge.
Unit 6_Introduction_Phishing_Password Cracking.pdf
Unit 6_Introduction_Phishing_Password Cracking.pdfKanchanPatil34 Initial stages of attacks, Phishing, Password Cracking, guidelines to prevent password attacks, Strong password, weak password,
Ultimate VMware 2V0-11.25 Exam Dumps for Exam Success
Ultimate VMware 2V0-11.25 Exam Dumps for Exam SuccessMark Soia Boost your chances of passing the 2V0-11.25 exam with CertsExpert reliable exam dumps. Prepare effectively and ace the VMware certification on your first try
Quality dumps. Trusted results. — Visit CertsExpert Now: https://www.certsexpert.com/2V0-11.25-pdf-questions.html
Presentation on Tourism Product Development By Md Shaifullar Rabbi
Presentation on Tourism Product Development By Md Shaifullar RabbiMd Shaifullar Rabbi Presentation on Tourism Product Development By Md Shaifullar Rabbi, Assistant Manager- SABRE Bangladesh.
apa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdfIshika Ghosh Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...
How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...Celine George Analytic accounts are used to track and manage financial transactions related to specific projects, departments, or business units. They provide detailed insights into costs and revenues at a granular level, independent of the main accounting system. This helps to better understand profitability, performance, and resource allocation, making it easier to make informed financial decisions and strategic planning.
Quality Contril Analysis of Containers.pdf
Quality Contril Analysis of Containers.pdfDr. Bindiya Chauhan Quality control test for containers, rubber closures and secondary packing materials.
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport This version of the lectures is provided free of charge to graduate students studying the Operations Management course at the MBA level.
Social Problem-Unemployment .pptx notes for Physiotherapy Students
Social Problem-Unemployment .pptx notes for Physiotherapy StudentsDrNidhiAgarwal Unemployment is a major social problem, by which not only rural population have suffered but also urban population are suffered while they are literate having good qualification.The evil consequences like poverty, frustration, revolution
result in crimes and social disorganization. Therefore, it is
necessary that all efforts be made to have maximum.
employment facilities. The Government of India has already
announced that the question of payment of unemployment
allowance cannot be considered in India
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...Library Association of Ireland
Introduction to Vibe Coding and Vibe Engineering
Introduction to Vibe Coding and Vibe EngineeringDamian T. Gordon Introduction to Vibe Coding and Vibe Engineering
How to Subscribe Newsletter From Odoo 18 Website
How to Subscribe Newsletter From Odoo 18 WebsiteCeline George Newsletter is a powerful tool that effectively manage the email marketing . It allows us to send professional looking HTML formatted emails. Under the Mailing Lists in Email Marketing we can find all the Newsletter.
UNIT 3 NATIONAL HEALTH PROGRAMMEE. SOCIAL AND PREVENTIVE PHARMACY
UNIT 3 NATIONAL HEALTH PROGRAMMEE. SOCIAL AND PREVENTIVE PHARMACYDR.PRISCILLA MARY J NATIONAL HEALTH PROGRAMMEE
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 795 from Texas, New Mexico, Oklahoma, and Kansas. 95 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...
Phoenix – A Collaborative Renewal of Children’s and Young People’s Services C...Library Association of Ireland
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 4-30-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
Introduction to Artificial Intteligence
- 1. Presentation on By BHARAT BHUSHAN Asst. Professor, Department of Computer Sc. RLSY College, Ranchi Buddha Science and Technical Institute, Ranchi www.bharatsir.com INTRODUCTION ARTIFICIAL INTELLIGENCE
- 2. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence UNIT-1 : INTRODUCTION Introduction to Artificial Intelligence Background and Applications Turing Test and Rational Agent approaches to AI Introduction to Intelligent Agents Their structure Behavior and environment.
- 3. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence The term artificial intelligence was first coined by John McCarthy in 1956 when he held the first academic conference on the subject.
- 4. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Introduction Artificial Intelligence (AI) is a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.
- 5. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Background of AI 1956 : John McCarthy coined the term ‘artificial intelligence’ and had the first AI conference. 1969 : Shakey was the first general-purpose mobile robot built. It is now able to do things with a purpose vs. just a list of instructions. 1997 : Supercomputer ‘Deep Blue’ was designed, and it defeated the world champion chess player in a match. It was a massive milestone by IBM to create this large computer.
- 6. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Background of AI 2002 : The first commercially successful robotic vacuum cleaner was created. 2005–2019 : Today, we have speech recognition, robotic process automation (RPA), a dancing robot, smart homes, and other innovations make their debut.
- 7. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Background of AI 2020 : Baidu releases the LinearFold AI algorithm to medical and scientific and medical teams developing a vaccine during the early stages of the SARS-CoV-2 (COVID-19) pandemic. The algorithm can predict the RNA sequence of the virus in only 27 seconds, which is 120 times faster than other methods.
- 8. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Applications of AI
- 9. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Applications of AI E-Commerce Education Lifestyle Navigation Robotics Human Resource Healthcare Agriculture Gaming Automobiles Social Media Marketing Chatbots Finance
- 10. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Turing Test In 1950, Alan Turing introduced a test to check whether a machine can think like a human or not, this test is known as the Turing Test. In this test, Turing proposed that the computer can be said to be an intelligent if it can mimic human response under specific conditions.
- 11. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Rational Agent A rational agent or rational being is a person or entity that always aims to perform optimal actions based on given premises and information. A rational agent can be anything that makes decisions, typically a person, firm, machine, or software.
- 12. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Example of Rational Agent Consider the case of this Vacuum cleaner as a Rational agent. It has the environment as the floor which it is trying to clean. It has sensors like Camera's or dirt sensors which try to sense the environment. It has the brushes and the suction pumps as actuators which take action.
- 13. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence How Does a Rational Agent Work? A rational agent is essentially a goal-based agent. It assesses its environment by considering what it is like. It then looks at each available action in its arsenal and determines how it will affect the environment and help it attain its goal. It tries out all the possible steps before choosing the best one, the one that will move it closest to its objective.
- 14. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence How Does a Rational Agent Work?
- 15. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Simple Diagram to understand Rational Agent A sensor could be a camera, an infrared device, a sonar, an ultrasound, a radar, or a lidar in the image above. It helps an AI robot determine an object’s or its surroundings’ size, identify a thing, and determine distances. An effector, meanwhile, is any device that affects a particular environment. It could be a robot’s legs, wheels, arms, fingers, wings, or fins. Effectors are also called “actuators.”
- 16. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Simple Diagram to understand Rational Agent A sensor could be a camera, an infrared device, a sonar, an ultrasound, a radar, or a lidar in the image above. It helps an AI robot determine an object’s or its surroundings’ size, identify a thing, and determine distances. An effector, meanwhile, is any device that affects a particular environment. It could be a robot’s legs, wheels, arms, fingers, wings, or fins. Effectors are also called “actuators.”
- 17. Bharat Bhushan, Assistant Professor, RLSY College, Ranchi Artificial Intelligence