State space search and Problem Solving techniques

Jul 25, 20202 likes2,113 views

here is my 4th ppt on state space search and problem solving techniques. must my watch my videos on my youtube channel techiseasy

◊ Problem Spaces.
◊ State Space.
◊ Searching Techniques.
◊ Uninformed search technique.
◊ Informed search techniques.
◊ Some AI problems.
In This Video

Problem Space
Define the problem in detail is known as problem
space. There are 4 things concerned to solve a
particular problem.
 Define the problem precisely.
 Analyze the problem.
 Isolate and represent the task knowledge i.e.
required to solve the problem.
 Selection of the best problem solving technique
and apply it. Like Tower of Hanoi, 8 puzzle games.

 Set of states and the
connections between
them to represent the
problem.
 Graph is used represent
problem.
 States are represented
by nodes of the graph,
and the operators by
edges between nodes.
 For simplicity it is
represented as trees,
where the initial state
is the root of the tree.
State Space

S:{S,A,Action(s),Result(s,a),Cost(s,a)}
Where,
S: set of states like start state, goal state,
etc.
A :set of actions available.
s: an individual state from S.
Action(s): action performed on s .
a: any action from set A.
Result(s, a): An intermediate state obtain
by performing a on s.
Cost(s, a):cost of executing action a on s.
branching factor, should be minimum.
State Space Search

Search Strategy
A strategy is defined by picking the order of node
expansion.
 Completeness- does it always find a solution if
one exists?
 Time complexity- no. of nodes generated.
 Space complexity- max no. of nodes in memory.
 Optimality- does it always find least cost
solution?
Time and space is measured in
‘b’ – max branching factor of search tree.
‘d’- depth of least cost solution.
‘m’ – max depth of the state space may be
(infinite)

Searching
Techniques
Uninformed
/Blind search
Informed
/Heuristic search
Searching Techniques

Uninformed Search
 Also known as Blind search.
 Problems require no domain specific knowledge.
 No idea, how to obtain solution, we apply Brute
force technique (explore every possible step).
 Time consuming.
 Time/Space Complexity is more.
For e.g., Travelling salesman problem for 5 cities
needs (n-1)! Search.
 Non polynomial method.
 Provides optimal solution.
 Methods: Breath first search (BFS), Depth first
search(DFS)

Informed Search
• Also known as Heuristic
search(estimation).
• Search with information.
• Heuristic function is used to find steps of
the solution.
• Provides quick solution.
• Less Time and space complexity.
• Does not produce optimal solution.
• Perform search in exponential time.
• Methods: A*, Heuristic DFS, Best first
search.

AI PROBLEMS
• Games:Water Jug problem ,8 Queen problem, 8
Puzzle problem
• Missionaries and Cannibals ,The Monday &
Bananas problem.
• Route finding.(computer networks, airline
travel planning system)
• Layout problems (VLSI layout, furniture
layout, packaging)
• Assembly sequencing.(Assembly of electrical
motors)
• Task Scheduling.(Time tables, manufacturing)

State space search and Problem Solving techniques

The document discusses several major programming languages used for artificial intelligence development: Python, C++, Java, Lisp, and Prolog. For each language, it provides an overview of its usage in AI, advantages such as libraries/tools available or speed, and disadvantages like complexity or lack of standardization. It notes that while no single language is best, the choice depends on the desired functionality and features of the AI application being developed.

Multiclass classification of imbalanced dataSaurabhWani6

Introduction to cyber security RaviPrashant5

Ø Information security is the protection of information from unauthorised access, use, disclosure or destruction through various means. This includes protecting both physical and electronic data. Ø Cyber security, also known as information security, aims to ensure the confidentiality, integrity and availability of information by protecting it from malicious attacks, damage or misuse when stored and accessed digitally. Ø As an employee, you are responsible for securing any information about customers, your organisation, colleagues and yourself to prevent misuse or unauthorized access according to the Data Protection Act 2018. This includes information stored electronically and in physical records.

Monte carloshishirkawde

Monte Carlo methods use random sampling to solve problems numerically. They work by setting up probabilistic models and running simulations using random numbers. This allows approximating solutions to problems in physics, finance, optimization, and other fields. Examples include estimating pi by simulating dart throws, and using a "drunken wino" random walk simulation to approximate the solution to a partial differential equation on a grid. The accuracy of Monte Carlo methods increases with more simulation iterations, requiring truly random numbers for best results.

Support Vector Machines ( SVM ) Mohammad Junaid Khan

Unit I discrete mathematics lecture notesGIRIM8

PreprocessorLearn By Watch

This document provides an overview of C programming language preprocessor directives. The preprocessor processes source code before it is passed to the compiler. Key preprocessor directives include: #define - Used for macro expansion to replace text in a program. Macros can take arguments like functions. #include - Used for file inclusion to incorporate the contents of one file into another. #ifdef - Used for conditional compilation to include or exclude code based on whether a macro is defined. The preprocessor allows code to be modularized across multiple files, constants to be defined via macros for improved performance, and single programs to be compiled conditionally for different environments. Preprocessor directives must be on their own lines and start with #.

Artificial Intelligence Searching TechniquesDr. C.V. Suresh Babu

Unit3:Informed and Uninformed searchTekendra Nath Yogi

The document discusses various search algorithms used in artificial intelligence including uninformed and informed search methods. It provides details on breadth-first search, depth-first search, uniform cost search, and heuristic search approaches like hill climbing, greedy best-first search, and A* search. It also covers general problem solving techniques and evaluating search performance based on completeness, time complexity, space complexity, and optimality.

Uninformed search /Blind search in AIKirti Verma

Lecture 06 production systemHema Kashyap

Production systems provide a structure for modeling problem solving as a search process. A production system consists of rules, knowledge databases, a control strategy, and a rule applier. The rules take the form of condition-action pairs. The control strategy determines the order of rule application and resolves conflicts. Production systems can be classified based on whether rule application is monotonic or non-monotonic. They provide modularity and a natural representation but can suffer from opacity, inefficiency, and lack of learning abilities. Choosing the right production system depends on characteristics of the problem such as decomposability and predictability.

State Space Search in aivikas dhakane

Solving problems by searchingLuigi Ceccaroni

Artificial Intelligence involves representing problems as state spaces and using algorithms to search the state space to solve the problem. The document discusses key concepts in problem solving using search including representing the problem as states, defining state transitions with successor functions, and exploring the resulting state space to find a solution. It provides examples of representing common problems like the 8-puzzle and n-queens as state spaces. The document also summarizes uninformed search strategies like breadth-first, depth-first, and iterative deepening search that use the problem definition to search the state space without using heuristics.

Planningahmad bassiouny

The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.

Adversarial searchNilu Desai

Adversarial search is a technique used in game playing to determine the best move when facing an opponent who is also trying to maximize their score. It involves searching through possible future game states called a game tree to evaluate the best outcome. The minimax algorithm searches the entire game tree to determine the optimal move by assuming the opponent will make the best counter-move. Alpha-beta pruning improves on minimax by pruning branches that cannot affect the choice of move. Modern game programs use techniques like precomputed databases, sophisticated evaluation functions, and extensive search to defeat human champions at games like checkers, chess, and Othello.

Local search algorithmMegha Sharma

Local search algorithms operate by examining the current node and its neighbors. They are suitable for problems where the solution is the goal state itself rather than the path to get there. Hill-climbing and simulated annealing are examples of local search algorithms. Hill-climbing continuously moves to higher value neighbors until a local peak is reached. Simulated annealing also examines random moves and can accept moves to worse states based on probability. Both aim to find an optimal or near-optimal solution but can get stuck in local optima.

AI_Session 10 Local search in continious space.pptxGuru Nanak Technical Institutions

This document summarizes key points from a session on problem solving by search in artificial intelligence. It discusses search strategies like breadth-first search, uniform cost search, depth-first search, greedy best-first search, and A* search. It also covers local search algorithms for continuous spaces, including hill climbing and simulated annealing. Examples of local maxima, plateaus, and ridges as problems for hill climbing are provided along with solutions like backtracking, random moves, and bidirectional search. The next session topics are on non-deterministic actions, partial observations, online search, and unknown environments.

Structure of agentsMANJULA_AP

The document discusses different types of AI agents based on their structure and capabilities: - Simple reflex agents take actions based solely on current percepts without considering history. Model-based reflex agents also track an internal state based on percept history. - Goal-based agents expand on model-based agents by considering goals and planning long sequences of actions to achieve goals. - Utility-based agents act not just on goals but the best way to achieve goals by considering the utility of different states. - Learning agents can improve their performance over time by learning from experiences, with components like a learning element, critic, and problem generator that help adapt automatically.

I.BEST FIRST SEARCH IN AIvikas dhakane

Search problems in Artificial Intelligenceananth

Hill climbingMohammad Faizan

This document discusses various heuristic search algorithms including generate-and-test, hill climbing, best-first search, problem reduction, and constraint satisfaction. Generate-and-test involves generating possible solutions and testing if they are correct. Hill climbing involves moving in the direction that improves the state based on a heuristic evaluation function. Best-first search evaluates nodes and expands the most promising node first. Problem reduction breaks problems into subproblems. Constraint satisfaction views problems as sets of constraints and aims to constrain the problem space as much as possible.

knowledge representation using rulesHarini Balamurugan

The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.

A* AlgorithmDr. C.V. Suresh Babu

Uninformed Search techniqueKapil Dahal

This document discusses several search strategies including uninformed search, breadth-first search, depth-first search, uniform cost search, iterative deepening search, and bi-directional search. It provides algorithms and examples to explain how each strategy works. Key points include: breadth-first search visits nodes by level of depth; depth-first search generates nodes along the largest depth first before moving up; uniform cost search expands the lowest cost node; and iterative deepening search avoids infinite depth by searching each level iteratively and increasing the depth limit.

A* Search Algorithmvikas dhakane

Control Strategies in AIAmey Kerkar

Informed search (heuristics)Bablu Shofi

1) The document discusses various search algorithms including uninformed searches like breadth-first search as well as informed searches using heuristics. 2) It describes greedy best-first search which uses a heuristic function to select the node closest to the goal at each step, and A* search which uses both path cost and heuristic cost to guide the search. 3) Genetic algorithms are introduced as a search technique that generates successors by combining two parent states through crossover and mutation rather than expanding single nodes.

Lecture 3-Problem Solving by Searching Techniques.pdfShahzad Ashraf

Artificial Intelligence Problem Slaving PPTvipulkondekar

More Related Content

What's hot (20)

Artificial Intelligence Searching TechniquesDr. C.V. Suresh Babu

Unit3:Informed and Uninformed searchTekendra Nath Yogi

Uninformed search /Blind search in AIKirti Verma

Lecture 06 production systemHema Kashyap

State Space Search in aivikas dhakane

Solving problems by searchingLuigi Ceccaroni

Planningahmad bassiouny

Adversarial searchNilu Desai

Local search algorithmMegha Sharma

AI_Session 10 Local search in continious space.pptxGuru Nanak Technical Institutions

Structure of agentsMANJULA_AP

I.BEST FIRST SEARCH IN AIvikas dhakane

Search problems in Artificial Intelligenceananth

Hill climbingMohammad Faizan

knowledge representation using rulesHarini Balamurugan

A* AlgorithmDr. C.V. Suresh Babu

Uninformed Search techniqueKapil Dahal

A* Search Algorithmvikas dhakane

Control Strategies in AIAmey Kerkar

Informed search (heuristics)Bablu Shofi

Artificial Intelligence Searching TechniquesDr. C.V. Suresh Babu

Unit3:Informed and Uninformed searchTekendra Nath Yogi

Uninformed search /Blind search in AIKirti Verma

Lecture 06 production systemHema Kashyap

State Space Search in aivikas dhakane

Solving problems by searchingLuigi Ceccaroni

Planningahmad bassiouny

Adversarial searchNilu Desai

Local search algorithmMegha Sharma

AI_Session 10 Local search in continious space.pptxGuru Nanak Technical Institutions

Structure of agentsMANJULA_AP

I.BEST FIRST SEARCH IN AIvikas dhakane

Search problems in Artificial Intelligenceananth

Hill climbingMohammad Faizan

knowledge representation using rulesHarini Balamurugan

A* AlgorithmDr. C.V. Suresh Babu

Uninformed Search techniqueKapil Dahal

A* Search Algorithmvikas dhakane

Control Strategies in AIAmey Kerkar

Informed search (heuristics)Bablu Shofi

More from Kirti Verma (20)

WORD SENSE DISAMIGUITION, methods to resolve WSD, wordnet,Kirti Verma

Probabilistic parsing, Parsing In NLP ,Context Free grammerKirti Verma

feature structure and unification NLP,what is Unification, Feture structure i...Kirti Verma

Grammar rules in English, Dependency Parsing, Shallow parsingKirti Verma

Role of Parser Words & Word Groups Parsing in NL, Types of parsing, Parsing A...Kirti Verma

Words _Transducers Finite state transducers in natural language processingKirti Verma

Regular-expressions in NLP and regular expression with exampleKirti Verma

NLP Introduction , applications, NLP Pipeline, Steps in NLPKirti Verma

L-5 BCEProcess management.pptKirti Verma

The document discusses key concepts related to process management in operating systems. It describes that an OS executes programs as processes, which can be in various states like running, waiting, ready etc. It also explains process control blocks that contain details of a process like state, registers, scheduling info etc. The document discusses process scheduling and synchronization techniques used by the OS to share CPU and other resources between multiple processes. It describes mechanisms for process creation, termination and interprocess communication using shared memory and message passing.

L-3 BCE OS FINAL.pptKirti Verma

This document provides an introduction to operating systems. It discusses what an operating system is, its key functions such as process management, memory management, file management, device management, and security. It describes the evolution of operating systems from early batch systems to modern multiprogramming, time-sharing, and distributed systems. Popular types of operating systems are also outlined, including desktop, server, mobile, and embedded operating systems. Key topics like kernels, system calls, computer architecture, and user interfaces are summarized as well.

L-4 BCE Generations of Computers final.pptKirti Verma

L-1 BCE computer fundamentals final kirti.pptKirti Verma

The document defines a computer and describes its key advantages such as speed, accuracy, storage capability, diligence, and versatility. It then discusses some disadvantages like lack of intelligence, dependency on humans, and lack of feelings. The document also provides overviews of several topics related to computing including e-business, bioinformatics, healthcare applications, remote sensing, geographic information systems, meteorology/climatology, and computer gaming. Finally, it describes the fundamental components of a computer including the CPU, memory subsystem, I/O subsystem, and how they are connected via buses. It provides details on registers, instruction format, and the instruction cycle.

BCE L-4 Data Type In C++.pptKirti Verma

BCE L-3 overview of C++.pptKirti Verma

BCE L-2 Algorithms-and-Flowchart-ppt.pptKirti Verma

The document discusses algorithms and flowcharts. It defines an algorithm as an ordered sequence of steps to solve a problem and notes that algorithms go through problem solving and implementation phases. Pseudocode is used to develop algorithms, which are then represented visually using flowcharts. The document outlines common flowchart symbols and provides examples of algorithms and corresponding flowcharts to calculate grades, convert between units of length, and calculate an area. It also discusses complexity analysis of algorithms in terms of time and space.

BCE L-1 Programmimg languages.pptxKirti Verma

The document discusses several programming paradigms including imperative, object-oriented, and declarative paradigms. Imperative programming uses procedures and functions to manipulate data, exemplified by languages like C and Pascal. Object-oriented programming revolves around objects and classes, promoting concepts like inheritance and encapsulation in languages such as Java and C++. Declarative programming treats computation as the evaluation of mathematical functions, emphasizing immutability and pure functions in languages like Haskell and Lisp. The document also outlines the six phases of the program development life cycle: problem definition, problem analysis, algorithm development, coding and documentation, testing and debugging, and maintenance.

BCE L-1 networking fundamentals 111.pptxKirti Verma

This document provides an overview of computer networks. It begins by defining a computer network as interconnecting two or more computer systems or peripheral devices to enable communication and sharing of resources. The key components of a network are identified as computers, cables, network interface cards, connecting devices, networking operating systems, and protocol suites. Advantages of networking include sharing hardware and software, increasing productivity through file sharing, backups, cost effectiveness, and saving time. Disadvantages include high installation costs, required administration time, single point of failure risk, cable faults interrupting connectivity, and security risks from hackers that require firewalls and antivirus software. The document discusses peer-to-peer and client-server network architectures and covers switching techniques like circuit

BCE L-2 e commerce.pptxKirti Verma

BCE L-3omputer security Basics.pptxKirti Verma

Computer security involves protecting computing systems and data from theft or damage. It ensures confidentiality, integrity, and availability of data. Common computer security threats include unauthorized access, hackers, viruses, and social engineering. Antivirus software, firewalls, and keeping systems updated help enhance security. Laws also aim to prevent cybercrimes like privacy violations, identity theft, and electronic funds transfer fraud. Overall computer security requires technical safeguards and vigilance from users.

L 5 Numpy final learning and CodingKirti Verma

NumPy is a Python library that provides multidimensional arrays and matrices for numerical computing along with high-level mathematical functions to operate on these arrays. NumPy arrays can represent vectors, matrices, images, and tensors. NumPy allows fast numerical computing by taking advantage of optimized low-level C/C++ implementations and parallel computing on multicore processors. Common operations like element-wise array arithmetic and universal functions are much faster with NumPy than with native Python.