introduction to Artificial Intelligence for computer science

DEBRE TABOR UNIVERSITY
FACULTY OF TECHNOLOGY
DEPARTMENT OF COMPUTER SCIENCE
Artificial Intelligence (CoSc4142)
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CHAPTER-ONE
Introduction to Artificial Intelligence

1.1 Goals of AI
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 Engineering Goal
 To solve real-world problems, build systems that show
intelligent behavior.
 Scientific Goal
 To understand what kind of computational mechanisms
are needed for modeling intelligent behavior.
The goal of Artificial Intelligence is to create intelligent
machines.

1.2 What is AI?
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 Intelligence:
 Intelligence is the capability of observing, learning,
remembering & reasoning.
Intelligence is not to make no mistakes but quickly to
understand how to make them good.

Characteristics of Intelligent system
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 Use vast amount of knowledge.
 Learn from experience and adopt to changing environment.
 Interact with human using language and speech.
 Respond in real time.
 Tolerate error and ambiguity in communication.

What Is Artificial Intelligence ?
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 The science of making computers intelligent
 The study of ideas that enable computers to be intelligent.
 Attempts to develop intelligent agents.
 The concern of AI :- to develop computer based system that behave
like human and emulate the reasoning power of humans

1.3 Approaches to AI – making computer:
1. Think like a human: The Cognitive Modeling
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 Reasons like humans do
 Programs that behave like humans
 Requires understanding of the internal activities of the brain.
Example.
 Instead of making the best possible chess-playing program, you
would make one that play chess like people do.

2. Act like a human: The Turing Test
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 Can machines act like human do? Can machines behave intelligently?
 Turing Test: Operational test for intelligent behavior
 Do experiments on the ability to achieve human-level performance.
 Acting like humans requires AI programs to interact with people.
 Major components of AI: knowledge, reasoning, language
understanding, learning.

3. Think rationally: The Laws of Thought
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 A system is rational if it thinks the right thing through correct reasoning.
 Aristotle: provided the correct arguments/ thought structures that always
gave correct conclusions given correct premises.
 Abebe is a man; all men are mortal; therefore Abebe is mortal
 These Laws of thought governed the operation of the mind and initiated the field of
Logic.

4. Acting rationally: The rational agent Approach
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 Doing the right thing: so as to achieve one’s goal, given one’s beliefs.
 AI is the study and construction of rational agents.
 Rational action requires the ability to represent knowledge and
reason with it so as to reach good decision.

Why AI?
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"AI can have two purposes”
 Use the power of computers to augment human thinking,
- just as we use motors to augment human or horse power.
- Robotics and expert systems are major branches of that.
 To understand how humans think in a humanoid way.

Applications of AI
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Solving problems that required thinking by humans:
 Playing games (chess, checker, cards, ...)
 Proving theorems (mathematical theorems, laws of physics, …)
 Classification of text (Politics, Economic, Social, Sports, etc.,)
 Information filtering and summarization of text
 Writing story and poems, solving puzzles
 Giving advice in Medical diagnosis, Equipment repair, Computer configuration and
Financial planning.

How to make computers act like humans?
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The following sub-fields are emerged
 Natural Language processing: (enable computers communicate in
human language, English,Amharic, ..)
 Knowledge representation: (schemes to store information, both facts and
inferences, before and during interrogation)
 Automated reasoning: (use stored information to answer questions and to
draw new conclusions)

Con’t…
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 Machine learning: (adapt to new circumstances and accumulate knowledge)
 Computer vision: (recognize objects based on patterns in the same
way as the human visual system does)
Robotics: (produce mechanical device capable of controlled motion with the
ability to move, see, hear, and accordingly take actions in the world, possibly
responding to new perceptions)

History of AI
 Maturation of Artificial Intelligence (1943-1952)
➢ 1943: The first work (AI ) was done by Warren McCulloch and Walter pits in 1943.
They proposed a model of artificial neurons.
➢ 1949: Donald Hebb demonstrated an updating rule for modifying the connection
strength between neurons. His rule is now called Hebbian learning.
➢ 1950: The Alan Turing who was an English mathematician and pioneered Machine
learning in 1950.
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Cont’d…
 The birth of Artificial Intelligence (1952-1956)
➢ 1955: Allen Newell and Herbert A. Simon created the "first artificial intelligence program"
Which was named "Logic Theorist".
This program had proved 38 of 52 Mathematics theorems.
 1956: The word "Artificial Intelligence" first adopted by American Computer scientist John
McCarthy at the Dartmouth Conference.
 FORTRAN, LISP, or COBOL were invented
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Cont’d…
 The golden years-Early enthusiasm (1956-1974)
 1966: The researchers emphasized developing algorithms that can solve mathematical problems.
 1972: The first intelligent humanoid robot was built in Japan which was named WABOT-1
 The first AI winter (1974-1980)
 The time period where computer scientists dealt with a severe shortage of funding from the
government for AI researches.
 During AI winters, an interest in publicity on artificial intelligence was decreased
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Cont’d…
 A boom of AI (1980-1987)
 1980: AI came back with "Expert System".
 1980, the first national conference of the American Association AI was held at Stanford University.
 The second AI winter (1987-1993)
 Again, Investors and government stopped in funding for AI research due to high cost but not efficient
results.
 The expert system such as XCON was very cost-effective.
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Cont’d…
 The emergence of intelligent agents (1993-2011)
 1997: IBM Deep Blue beats world chess champion, Gary Kasparov, became the first
computer to beat a world chess champion.
 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner
 2006: AI came into the Business world.
 Companies like Facebook, Twitter, and Netflix also started using AI.
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Cont’d…
 Deep learning, big data and artificial general intelligence (2011-present)
 2011: IBM's Watson won jeopardy, a quiz show, where it had to solve complex questions as well
as riddles. .
 2012: Google has launched an Android app feature "Google now", which was able to provide
information to the user as a prediction.
 2014: Chatbot "Eugene Goostman" won a competition in the infamous "Turing test."
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AI in Ethiopia
 Getnet Assefa is a scientist and co-founder of iCog Labs, an
AI research space in Addis Ababa.
 iCog was part of a team of scientists that developed the
software for Sophia – the world’s first humanoid robot.
 Sophia has even learnt some Amharic, the developers say.
 can display 60 types of facial expressions and carry a
conversation.
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Cont’d…
 iCog has customers in USA, Canada, Hong Kong and China and at
home, works with the government on software and hardware
development.
 D.r Timnit Gebru is an Eritrean American computer scientist and the
technical co-lead of the Ethical Artificial Intelligence Team at Google.
 She works on algorithmic bias and data mining. She is an advocate for
diversity in technology and is the cofounder of Black in AI, a
community of black researchers working in artificial intelligence.
 Timnit is an Eritrean origin born and raised in Ethiopia
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CHAPTER : 2
Intelligent Agents
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Agents and Environments
 An agent: is anything that can be viewed as perceiving its environment
through sensors and acting upon that environment through
actuators/effectors.
Figure 1:Agents interact with environments through sensors and effectors.
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Con’t…
 A human agent: has eyes, ears, and other organs for sensors and
hands, legs, vocal tract, and so on for actuators.
 A robotic agent: might have cameras and infrared range finders for
sensors and various motors for actuators.
 A software agent: receives keystrokes, file contents, and network
packets as sensory inputs and acts on the environment by displaying on
the screen, writing files, and sending network packets.
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Intelligent agents
 An intelligent agent: is a system that perceives its environment,
learns from it, and interacts with it intelligently.
 Intelligent agents can be divided into two broad categories:
software agents and physical agents.
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Software agent
 A software agent is a set of programs that are designed
to do particular tasks.
 For example, a software agent is a search engine used
to search theWorldWideWeb and find sites that can
provide information about a requested subject.
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Physical agent
 A physical agent (robot) is a programmable system that can
be used to perform a variety of tasks.
 Simple robots can be used in manufacturing to do routine
jobs such as assembling, welding, or painting.
 Some organizations use mobile robots that do routine
delivery jobs such as distributing mail or correspondence to
different rooms.
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Acting of Intelligent Agents (Rationality)
 An agent should strive to "do the right thing", based on
what it can perceive and the actions it can perform.
 The right action is the one that will cause the agent to be
most successful.
 Performance measure:An objective criterion for success of
an agent's behavior.
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Con’t…
 E.g. performance measure of a vacuum-cleaner agent could be
amount of dirt cleaned up, amount of time taken, amount of
electricity consumed, amount of noise generated, etc.
 Rational Agent: For each possible percept sequence, a rational
agent should select an action that is expected to maximize its
performance measure, given the evidence provided by the
percept sequence and whatever built-in knowledge the agent has.
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Con’t…
 Agents can perform actions in order to modify future
percepts so as to obtain useful information (information
gathering, exploration)
 An agent is autonomous if its behavior is determined by its
own experience (with ability to learn and adapt)
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Con’t…
 In summary what is rational at any given point depends on
PEAS (Performance measure, Environment,Actuators,
Sensors) framework.
 Performance measure
The performance measure that defines degrees of
success of the agent
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Con’t…
 Environment
 Knowledge:What an agent already knows about the environment
 Actuators – generating actions
The actions that the agent can perform back to the environment
 Sensors – receiving percepts
Perception: Everything that the agent has perceived so far
concerning the current scenario in the environment
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Example: PEAS
 Consider the task of designing an automated taxi driver agent:
 Performance measure: Safe, fast, legal, comfortable trip, maximize
profits.
 Environment: Roads, other traffic, pedestrians, customers
 Actuators: Artificial legs & hands, Speaker
 Sensors: Cameras, GPS, engine sensors, recorder (microphone)
 Goal: driving safely from source to destination point
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Structure of agents
 Agent = architecture + program
 Architecture = some sort of computing device (sensors + actuators)
 (Agent) Program = some function that implements the agent
mapping.
 Agent Program = Job of AI
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Agent programs
 Input for Agent Program
 Only the current percept.
 Input for Agent Function
 The entire percept sequence.
 The agent must remember all of them.
 Implement the agent program as
 A look up table (agent function)
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Types of agent programs
1. Simple Reflex Agents
2. Model-Based Reflex Agent
3. Goal based agents
4. Utility based agents
5. Learning Agents
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1. Simple Reflex Agents
 It works by finding a rule whose condition matches the current situation (as
defined by the percept) and then doing the action associated with that rule.
 These agents select actions on the basis of the current percept, ignoring the
rest of the percept history.
 It uses just condition-action rules
 The rules are like the form “if … then …”
 Because knowledge sometimes cannot be stated explicitly
 Work only
 if the environment is fully observable
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Con’t…
Example:Automated taxi driving agent.
 If the car in front brakes and its brake lights on, then you should notice this
and initiate braking.This can be written as condition-action rule:
 If car-in-front-is-braking then initiate-braking;
 Humans also have many such connections, some of which are learned
responses (as for driving) and some of which are innate reflexes (such as
blinking when something approaches the eye).
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Structure of a simple reflex agent
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Con’t…
Limitation of simple reflex agent:
 The agent will work only if the environment is fully observable that is, it will
work only if the correct decision can be made on the basis of only the current
percept. Even a little bit of unobservability can cause serious trouble.
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2. Model-Based ReflexAgent
 It works by finding a rule whose condition matches the current situation/state.
 For the world that is partially observable
 The agent has to keep track of an internal state
 That depends on the percept history
 Reflecting some of the unobserved aspects
 E.g. driving a car and changing track
 Requiring two types of knowledge
 How the world evolves independently of the agent
 How the agent’s actions affect the world
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Con’t…
 Note: Regardless of the kind of representation used, it is seldom possible
for the agent to determine the current state of a partially observable
environment exactly. It can do only “best guess”.
Example:An automated taxi may not be able to see around the large truck
that has stopped in front of it and can only guess about what may be
causing the hold-up.
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Structure of Model-Based reflex agent
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3. Goal based agents
 Knowing something about the current state of the environment is not always
enough to decide what to do.
 Example:At a road junction, the taxi can turn left, turn right, or go straight on.
The correct decision depends on where the taxi is trying to get it.
 The goal is another issue to achieve
 Judgment of rationality / correctness
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Structure of a Goal-based agent
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Con’t…
 Conclusion
 Goal-based agents are less efficient
 but more flexible, because the knowledge that supports its decision is
represented explicitly and can be modified.
 Agent  Different goals  different tasks
 Search and planning
 Two other sub-fields in AI
 To find out the action sequences to achieve its goal
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4. Utility based agents
 Goals alone are not enough to generate high-quality behavior in most environments.
Example: Many action sequences will get the taxi to its destination (thereby achieving the
goal) but some are quicker, safer, more reliable, or cheaper than others.
 Many action sequences  the goals
 Some are better and some worse
 If goal means success.
 Then utility means the degree of success (how successful it is)
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Con’t…
 It is said stateA has higher utility
 If state A is more preferred than others
 Utility is therefore a function
 That maps a state onto a real number
 The degree of success
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Structure of a utility-based agent
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5. Learning Agents
 After an agent is programmed, can it work immediately?
 No, it still need teaching
 InAI
 Once an agent is done
We teach it by giving it a set of examples
Test it by using another set of examples
 We then say the agent learns
 A learning agent
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Con’t…
 Four conceptual components
 Learning element: Responsible for making improvements.
 Performance element: Responsible for selecting external actions.
 Critic:Tells the Learning element how well the agent is doing with
respect to fixed performance standard.
 Problem generator: Suggest actions that will lead to new and
informative experiences.
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Structure of Learning Agents
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Quiz one 5%
1. Why an agent uses sensor and actuator?
2. What does it mean AI?
3. Do you think that are you an intelligence? If you say
yes or No? Why and what are the characteristics of
you?
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CHAPTER - 3
SOLVING PROBLEMS BY SEARCHING AND
CONSTRAINT SATISFACTION PROBLEM
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By: Wondifraw Manaye

INTRODUCTION
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 What is a Problem?
 It is a gap between what actually is and what is desired.
 A problem exists when an individual becomes aware of the existence of a
significant difference between the expected and the actual situation, which
is an obstacle and makes it difficult to achieve a desired goal or objective.

Con’t…
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 Problem solving by searching: find a sequence of actions that achieve its
goals when no single action will do.
 Goal based agents: consider future actions + desirability of their outcomes.
 A problem-solving agent: is a kind of goal based agent.
 Planning Agents: Goal based agents that use more advanced factored or
structured representations are called planning agents.

Problem-Solving Agents
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 It is known as, if the agent can adopt a goal and satisfying it.
 Goals help to organize behavior by limiting the objectives that the agent
is trying to achieve and hence the actions it needs to consider.

Goal Formulation
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current situation + agent’s performance measure.
The first step in problem solving.
 We will consider a goal to be a set of world states exactly
those states in which the goal is satisfied.
 The agent’s task is to find out how to act, now and in the future,
so that it reaches a goal state.

Problem Formulation
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 It is the process of deciding what actions and states
to consider, given a goal.

In our example, we assume that the environment is:-
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 Observable:- the agent always knows the current state.
 For the agent driving in Romania, it’s reasonable to suppose that each
city on the map has a sign indicating its presence to arriving drivers.
 Discrete:- at any given state there are only finitely many actions to
choose from.
 This is true for navigating in Romania because each city is connected to
a small number of other cities.

Con’t…
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 Known:- the agent has known which states are reached by each action.
 Having an accurate map suffices to meet this condition.
 Deterministic:- each action has exactly one outcome.
 It means that if an agent chooses to drive from Arad to Sibiu, it does
end up in Sibiu.
Search: The process of looking for a sequence of actions that reaches the
goal is called search.

Con’t…
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Solution: A search algorithm takes a problem as input and returns a
solution in the form of an action sequence.
Execution: Once a solution is found, the action it recommends can be
carried out. This is called the execution phase.
Open-Loop: While the agent is executing the solution sequence it ignores its
percepts when choosing an action because it knows in advance what they
will be is called open-loop system, because ignoring the percepts breaks
the loop between agent and environment.

Fig. 1 A simple problem solving agent. It first formulates a goal and a problem,
searches for a sequence of actions that would solve the problem, and then executes
the actions one at a time. When this is complete, it formulates another goal and starts
over
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Well-defined problems and solutions
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A problem can be defined formally by five components.
1. Initial State: That the agent starts in.
 In our example, the initial state for our agent in Romania might be described
as In (Arad).
2. Actions: A description of possible actions available to the agent.
 Given a particular state s, ACTIONS (s) returns the set of actions that can
be executed in s. We say that each of these actions is applicable in s.
 In our example, from the state In (Arad), the applicable actions are
{Go (Sibiu), Go (Timisoara), Go (Zerind)}

Con’t…
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3. Transition model or Successor: A description of what each action
does, specified by a function.
 RESULT (s, a) that returns the state that results from doing action a
in state s.
 In our example, RESULT (In (Arad), Go (Zerind)) = In (Zerind).
 Together the initial state, actions and transition model implicitly define
the state space of the problem the set of all states reachable from the
initial state by any sequence of actions.

Con’t…
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4. Goal Test: Which determines whether a given state is a goal state.
 Sometimes there is an explicit set of possible goal states, and the test simply checks whether
the given state is one of them.
 In our example, the agent’s goal in Romania is the singleton set {In (Bucharest)}.
5. Path Cost: A function that assigns a numeric cost to each path.
 For the agent trying to get to Bucharest, time is of the essence, so the cost of a path might
be its length in kilometers.
 We assume that the cost of a path can be described as the sum of the costs of the
individual actions along the path. The step cost of taking action a in state s to reach state s1

For example, consider the map of Romania in Figure.2
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The problem formulation is therefore:
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Initial state: at Arad
Actions: the successor function S:
 S(Arad) = {<AradZerind, Zerind>, <AradSibiu, Sibiu>,<AradTimisoara,
Timisoara}
 S(Sibiu) = {<SibiuArad, Arad>, <SibiuOradea, Oradea>, < Sibiu Fagaras,
Fagaras>, SibiuRimnicu Vilcea, Rimnicu Vilcea>}, etc.
Goal test: at Bucharest
Path cost: c(Arad, AradZerind, Zerind) = 75, c(Arad, AradSibiu, Sibiu) = 140,
c(Arad, Arad Timisoara, Timisoara) = 118, etc.

Con’t…
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 For example, if the agent is in the state Arad, there are 3 possible
actions, Arad  Zerind, Arad  Sibiu and Arad  Timisoara,
resulting in the states Zerind, Sibiu and Timisoara respectively.
 The solution to drive from Arad to Bucharest is Arad to Sibiu to
Rimnicu Vicea to Pitesti to Bucharest must be a solution.

Example problems:
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 Toy problem: is intended to illustrate or exercise various problem solving
methods.
 It can be given a brief, exact description and hence is usable by
different researchers to compare the performance of algorithms.
 Real world problems: is one whose solutions people actually care about.
 Such problem tends not to have a single agreed-upon description, but we
can give the general flavor/taste of their formulations.

Toy problems:
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1. 8-Puzzle: Consists of a 3×3 board with eight numbered tiles and a
blank space. A tile adjacent to the blank space can slide into the
space. The objective is to reach a specified goal state.

Con’t…
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 States: A state description specifies the location of each of the eight tiles
and the blank in one of the nine squares.
 Initial State: Any state can be designated as the initial state. Note that any
given goal can be reached from exactly half of the possible initial states.
 Actions: The simplest formulation defines the action on movements of the
blank space Left, Right, Up or Down.
 Transition Model: Given a state and action, this returns the resulting state.
Example: If we apply left to the start state in figure, the resulting state has
the 5 and the blank switched.

Con’t…
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 Goal Test: This checks whether the state matches the goal
configuration as shown in figure.
 Path cost: Each cost costs 1, so the path cost in the number of steps
in the path.
Note: The 8-puzzle belongs to the family of sliding-block puzzles,
which are often used as test problems for new search algorithms in
AI.

Real World problems:
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1. Route-finding problem: is defined in terms of specified
locations and transitions along links between them.
 Route-finding algorithms are used in a variety of applications
such as, websites, in car systems to provide driving directions,
routing video streams in computer networks, military operations
planning, airline travel planning systems.

Consider the air line travel problems: that must be solved by a travel-
planning web site:
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States: Each state obviously includes a location (airport) and the current
time. Furthermore, the state must record extra information like, base
fare, flight segment, their status as domestic or international, to
decide the cost of an action.
Initial state: This is specified by the user’s query.
Actions: Take any flight from the current location, in any seat class,
leaving the current time, leaving enough time for within airport
transfer if needed.

Con’t…
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Transition model: The state resulting from taking a flight will have the
flight’s destination as the current location and the flight’s arrival time as
the current time.
Goal Test: Are the final destination specified by the user?
Path cost: This depends on monetary cost, waiting time, flight time,
customs and immigration procedures, seat quality, time of day, type of
airplane, frequent flyer mileage awards and so on.

Chapter Four
Knowledge and Reasoning
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Knowledge:What andWhy?
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 Knowledge includes facts about the real world entities and the relationship
between them.
Why Knowledge is important?
 We are living in complex environment where there are:
• Many actors, prosumers, strong competitors, and high turn over
 It enables to:
• Automate reasoning , Discover new facts, Deduce new facts that follow from
the KB, and Answer users queries
• Make quality decisions - select courses of actions, etc.

Knowledge Base Agent
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 Knowledge base agent is an agent that perform action using the knowledge
it has and reason about their action using its inference procedure.
 Consists of two parts:
 knowledge base: contains the domain-specific knowledge that the agent
has of its environment. This can consist of facts, but also rules that describe
the structure of the environment.
 Inference engine: It consists of algorithms that take the contents of the
knowledge base and infer (i.e. deduce) new knowledge about the world.

Con’t…
85
 Knowledge base is a set of representation of facts and their
relation ships called rules about the world
 Each fact/rules are called sentences which is represented using a
language called knowledge representation language.

Con’t…
86
 Declarative approach to building an agent (or other system):
 Tell it what it needs to know (Knowledge base)
 Ask what it knows
 Answers should follow from the KB
 The agent must be able to:
 Represent states of the world, actions, etc.
 Incorporate new percepts (facts and rules)
 Deduce hidden properties of the world
 Deduce appropriate actions
 Update internal representations of the world

Logical Agents
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 Logic is the study of principles of reasoning and arguments towards the
truth of a given conclusion given premises.
 Logic in AI is the key idea for KB design, KB representation and
inferencing (reasoning).
 Logic is formal languages use for representing information so that
conclusions can be drawn.
 Logic is the systematic study of the general conditions of valid
inferences.

Con’t…
88
 A logic consists of a syntax and some semantics.
 The syntax defines what an allowable sentence is in the language, whereas
the semantics defines the meaning of the sentences.
 There are many different logics. For example, integer arithmetic is a logic. The
syntax of integer arithmetic states that the following are all legal sentences:
1 + 4 = x
x – 4 > 2
x2 – 2x = 0
 whereas the following are illegal sentences:
1 + * 2 =
x2 + y > {}

Entailment
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 The idea that a sentence follows logically from another sentence.
 α╞ β (i.e, the sentence α entails the sentence β; (or) the sentence β
logically follows from sentence α)
 The formal definition of entailment is this: α ╞ β if and only if, in
every model in which α is true, β is also true.
It can be written as, α ╞ β if and only if m(α) m (β).

Models
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 The semantics of a logic define the truth (or otherwise) of sentences in
possible worlds.
 A world is an assignment of values to variables.
For example: we can say that the integer arithmetic sentence “x + y = 4”
is true in the world where x = 2 and y = 2.
- Logicians often refer to models rather than worlds, but the meaning is the
same: an assignment of values to variables.
Definition: A model M is a model of a sentence α if α is true in M

Con’t…
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For example: The model {x = 2, y = 2} is a model of the sentence
“x + y = 4”.
 In addition, the model {x = 1, y = 3} is also a model of the
sentence “x + y = 4”. In fact, because there are an infinite number
of integers, there are an infinite number of models of this sentence.
Definition: M(α) is the set of all models of α

Propositional(Boolean) Logic
92
 Proposition is statement which is either true or false but not both at
any time.
 A statement is a sentence which is either true or false.
 PL uses declarative sentences only
 PL doesn’t involve quantifiers.
 Not all sentences are statement (interrogatives, imperatives and
exclamatory)

Con’t…
93
 Preposition can be conditional or unconditional
 Examples
 Socrates is mortal (unconditional)
 If the winter is severe, students will not succeed. (conditional)
 All are the same if their color is black (conditional)
 In propositional logic, symbols represent the whole preposition.
 Examples:
 M = Socrates is mortal
 W = winter is sever
 S = students will not succeed

Con’t…
94
 Preposition symbols can be combined using Boolean
connectives to generate new preposition with complex meaning
 Symbols involved in PL:
 Logical constants (TRUE and FALSE)
 Preposition symbols (also called atomic symbols) like M, W, S
 Logical connectives
 (negation),
 (conjunction),
 (disjunction),
 (bi-implication or equivalence),
 (implication) and parenthesis

Con’t…
95
 A simple language useful for showing key ideas and definitions
 User defines a set of propositional symbols, like P and Q.
 User defines the semantics of each propositional symbol:
P means “It is hot”
Q means “It is humid”
R means “It is raining”

Con’t…
96
 A sentence (well formed formula) is defined as follows:
 A symbol is a sentence
 If S is a sentence, then S is a sentence
 If S is a sentence, then (S) is a sentence
 If S and T are sentences, then (S  T), (S  T), (S  T), and (S ↔
T) are sentences
 A sentence results from a finite number of applications of the
above rules

Examples of PL sentences
97
 (P  Q)  R
“If it is hot and humid, then it is raining”
 Q  P, “If it is humid, then it is hot”
 Q , “It is humid.”
 A better way:
Ho = “It is hot”
Hu = “It is humid”
R = “It is raining”

Some terms
98
 The meaning or semantics of a sentence determines its interpretation.
 Given the truth values of all symbols in a sentence, it can be “evaluated”
to determine its truth value (True or False).
 A model for a KB is a “possible world” (assignment of truth values to
propositional symbols) in which each sentence in the KB is True.

Knowledge Representation and reasoning
101
 Knowledge Representation: express knowledge explicitly in a
computer-tractable way such that the agent can reason out.
 Parts of KR language:
 Syntax of a language: describes the possible configuration to form
sentences.
E.g. if x & y denote numbers, then x > y is a sentence about numbers

Con’t…
102
 Semantics: determines the facts in the world to which the sentences refer.
E.g. x > y is false when y is greater than x and true otherwise
Reasoning: is the process of constructing new sentences from existing facts
in the KB.
 Proper reasoning ensures that the new configuration represent facts that
actually follow from the facts in the KB.

Knowledge-based Systems and Knowledge
103
What is a knowledge-based system?
 A system which is built around a knowledge base. i.e. a
collection of knowledge, taken from a human, and stored in
such a way that the system can reason with it.
What is knowledge?
 Knowledge is the sort of information that people use to solve
problems.

Con’t...
104
 Knowledge includes:
 facts, concepts, procedures, models, heuristics, examples.
 Knowledge may be:
 specific or general
 exact or fuzzy
 procedural or declarative

Why would we want an agent to learn?
There are three main reasons:
 The designers cannot anticipate all possible situations that
the agent might find itself in.
 The designers cannot anticipate all changes over time.
 Sometimes human programmers have no idea how to program
a solution themselves.
108

Learning
 essential for unknown environments.
 useful as a system construction method.
modifies the agent's decision mechanisms to improve
performance
109

Learning agents consist of four main components:
 Learning element: the part of the agent responsible for
improving its performance
 Performance element: the part that chooses the actions to take
 Critic: provides feedback for the learning element how the agent
is doing with respect to a performance standard
 Problem generator: suggests actions that could lead to new,
informative experiences
111

Forms of learning: (factors for designing a learning agent)
 Any component of an agent can be improved by learning from
data. The improvements depend on four major factors.
 Which components of the performance element are to be
learned.
 What prior knowledge the agent already has.
 What feedback is available to learn these components.
 What representation is used for the components.
112

Types of Learning
 Supervised learning: The agent observes some example input-output pairs
and learns a function that maps from input to output, correct answer for
each.
Example. Answer can be a numeric variable, categorical variable etc.
 Unsupervised learning: correct answers not given – just examples (e.g. –
the same figures as above , without the labels)
 Reinforcement learning: the agent learns from a series of reinforcements-
rewards or punishments.
M M M
F F F
113

ARTIFICIAL NEURAL NETWORKS
131

Features of the Brain
 ten billion (1010) neurons in our brain
 Neuron switching time >10-3secs
 Face Recognition ~0.1secs
 each neuron has several thousand connections
 Hundreds of operations per second
 High degree of parallel computation
133

Neural Network
layered set of interconnected processors.
 These processor nodes has a relationship with the
neurons of the brain.
 Each node has a weighted connection to several other
nodes in adjacent layers.
 Individual nodes take the input received from connected
nodes and use the weights together to compute output
134

Con’t…
 The inputs are fed simultaneously into
the input layer.
 The weighted outputs of these units
are fed into hidden layer.
 The weighted outputs of the last
hidden layer are inputs to units making
up the output layer.
135

Architecture of Neural network
 Neural networks are used to look for patterns in data, learn
these patterns, and then classify new patterns & make
forecasts
 single-layered neural network: A NW with the input and output
layer only
 multilayer neural network is a generalized one with one or more
hidden layer.
136

Con’t…
 A network containing two hidden layers is called a
three-layer neural network, and so on.
137

A Multilayer Neural Network
 INPUT: records with class attribute with
normalized attributes values.
 INPUT VECTOR: X = { x1, x2, …. xn}, where n
is the number of attributes.
INPUT LAYER – there are as many nodes as
class attributes i.e. as the length of the input
vector.
138

Con’t…
 HIDDEN LAYER – neither its input nor its output can
be observed from outside.
 OUTPUT LAYER – corresponds to the class attribute.
139

Pros and Cons of Neural Network
 Pros
+ Can learn more complicated class boundaries
+ Fast application
+ Can handle large number of features
 Cons
 Slow training time
 Hard to interpret
 Hard to implement: trial and error
for choosing number of nodes
• Useful for learning complex data like handwriting, speech and image
recognition
• Neural Network needs long time for training.
• Neural Network has a high tolerance to noisy and incomplete data
140

CHAPTER-SEVEN
COMMUNICATING, PERCEIVING, AND ACTING
142

Natural Language Processing (NLP)
143
 The process of computer analysis of input provided in a human
language (natural language), and conversion of this input into a
useful form of representation.
 NLP is primarily concerned with getting computers to perform
useful and interesting tasks with human languages.
 NLP is secondarily concerned with helping us come to a
better understanding of human language.

Forms of Natural Language
144
 The input/output of a NLP system can be:
 written text
 speech
 We will mostly concerned with written text (not speech).
 To process written text, we need:
 Lexical, syntactic, semantic knowledge about the language
 discourse information, real world knowledge
 To process spoken language, we need everything required to
process written text, plus the challenges of speech recognition and
speech synthesis.

Components of NLP
145
1. Natural Language Understanding
 Taking some spoken/typed sentence and working out what it means
 Mapping the given input in the natural language into a useful
representation.
 Different level of analysis required:
morphological analysis,
syntactic analysis,
semantic analysis,
discourse analysis, …

Con’t…
147
2. Natural Language Generation
 Taking some formal representation of what you want to say and
working out a way to express it in a natural (human) language (e.g.,
English).
 NLG can be viewed as the reverse process of NL understanding.
 A NLG system may have two main parts:
 Discourse Planner - what will be generated. which sentences.
 Surface Realizer - realizes a sentence from its internal representation.
 Lexical Selection - selecting the correct words describing the
concepts.

Applications of NLP
 Machine Translation
 Database Access
 Information Retrieval
 Selecting from a set of documents the ones that are relevant to a query
 Text Categorization
 Sorting text into fixed topic categories
 Extracting data from text
 Converting unstructured text into structure data
 Spelling and grammar checkers
149

Why NL Understanding is hard?
150
 Natural language is extremely rich in form and structure, and very ambiguous.
 How to represent meaning,
 Which structures map to which meaning structures.
 One input can mean many different things. Ambiguity can be at different levels.
 Lexical (word level) ambiguity - different meanings of words
 Syntactic ambiguity - different ways to parse the sentence
 Interpreting partial information - how to interpret pronouns
 Contextual information - context of the sentence may affect the meaning of that sentence
 Many input can mean the same thing.
 Interaction among components of the input is not clear.

Knowledge of Language
151
Phonology – concerns how words are related to the sounds that realize them.
Morphology – concerns how words are constructed from more basic meaning units
called morphemes.
- A morpheme is the primitive unit of meaning in a language.
Syntax – concerns how can be put together to form correct sentences and
determines what structural role each word plays in the sentence and what
phrases are subparts of other phrases.
Semantics – concerns what words mean and how these meaning combine in
sentences to form sentence meaning.
- The study of context-independent meaning.

Con’t…
152
Pragmatics – concerns how sentences are used in different situations and
how use affects the interpretation of the sentence.
Discourse – concerns how the immediately preceding sentences affect
the interpretation of the next sentence. For example, interpreting
pronouns and interpreting the temporal aspects of the information.
World Knowledge – includes general knowledge about the world. What
each language user must know about the other’s beliefs and goals.

Ambiguity
I made her duck.
 How many different interpretations does this sentence have?
 What are the reasons for the ambiguity?
 The categories of knowledge of language can be thought of as
ambiguity resolving components.
 How can each ambiguous piece be resolved?
 Does speech input make the sentence even more ambiguous?
 Yes – deciding word boundaries
153

Con’t…
 Some interpretations of : I made her duck.
1. I cooked duck for her.
2. I cooked duck belonging to her.
3. I created a toy duck which she owns.
4. I caused her to quickly lower her head or body.
5. I used magic and turned her into a duck.
 duck – morphologically and syntactically ambiguous: noun or verb.
 her – syntactically ambiguous: dative or possessive.
 make – semantically ambiguous: cook or create.
 make – syntactically ambiguous:
 Transitive – takes a direct object. => 2
 Di-transitive – takes two objects. => 5
 Takes a direct object and a verb. => 4
154

Resolve Ambiguities
 We will introduce models and algorithms to resolve ambiguities at
different levels.
 part-of-speech tagging - Deciding whether duck is verb or noun.
 word-sense disambiguation - Deciding whether make is create or cook.
 lexical disambiguation - Resolution of part-of-speech and word-sense
ambiguities are two important kinds of lexical disambiguation.
 syntactic ambiguity - her duck is an example of syntactic ambiguity, and
can be addressed by probabilistic parsing.
155

Natural Language for Communication
156
 The goal in the production and comprehension of natural language is for
communication.
 Communication for the speaker:
Intention: Decide when and what information should be transmitted (strategic
generation).
- May require planning and reasoning about agents’ goals and beliefs.
Generation: Translate the information to be communicated into string of words in
desired natural language (tactical generation).
Synthesis: Output the string in desired modality, text or speech.

Con’t…
 Communication for the hearer:
Perception: Map input modality to a string of words, e.g. optical character
recognition (OCR) or speech recognition.
Analysis: Determine the information content of the string.
Syntactic Interpretation (parsing): Find the correct parse tree showing the phrase structure
of the string.
Semantic Interpretation: Extract the (literal) meaning of the string (logical form).
Pragmatic Interpretation: Consider effect of the overall context on altering the literal
meaning of a sentence.
Incorporation: Decide whether or not to believe the content of the string and
add it to the KB.
157

Speech Recognition
 Speech is the dominant modality for communication between
humans, and promises to be important for communication between
humans and machines.
 Speech recognition: is the task of mapping from a digitally
encoded acoustic signal to a string of words.
 Speech understanding: is the task of mapping from the acoustic
signal all the way to an interpretation of the meaning of the word.
158

Con’t…
A speech understanding system must answer three questions:
1. What speech sounds did the speaker utter?
2. What words did the speaker intend to express with those
speech sounds?
3. What meaning did the speaker intend to express with those
words?
159

Con’t…
161
 Frequency spectrogram
 Basic sounds in the signal (40-50 phonemes)
(e.g. “a” in “cat”)
 Template matching against db of phonemes
 Constructing words from phonemes
(e.g. “th”+”i”+”ng”=thing)
 Words

Speech Recognition-Complications
 No simple mapping between sounds and words
 Variance in pronunciation due to gender, dialect, …
 Restriction to handle just one speaker
 Same sound corresponding to different words
 e.g. bear, bare
 Finding gaps between words
 “how to recognize speech”
 “how to wreck a nice beach”
 Noise
162

Perception
 Perception provides an agent with
information about the world they inhabit
 Provided by sensors
 Anything that can record some aspect of the
environment and pass it as input to a program
163

Con’t…
 There are basically two approaches for perception
 Feature Extraction
 Detect some small number of features in sensory input and pass them to
their agent program
 Agent program will combine features with other information
 “bottom up”
 Model Based
 Sensory stimulus is used to reconstruct a model of the world.
 Start with a function that maps from a state of the world to a stimulus
 “top down”
164

Con’t…
 In reality, both feature extraction and model-based approaches
are needed.
 Not well understood how to combine these approaches
 Knowledge representation of the model is the problem
165

Robotic
 Robots are physical agents that perform tasks by manipulating the
physical world.
- Equipped with Effectors such as legs, wheels, joints, and
grippers.
- Also equipped with Sensors to perceive their environment,
sensors include cameras and ultrasound to measure the
environment’.
166

Types of Robots
167
1.Manipulators: Manipulators or robot arms are physically anchored to
their workplace, for example robots in a factory assembly line.
2.Mobile robots: Mobile robots move around their environment using
wheels, legs or similar mechanisms.
3.Hybrid type robots: Mobile robot equipped with manipulators, these
include the humanoid robot, whose physical design mimics the human
torso.

Modern uses of Robots
1. Explorations:
168

Con’t…
2. Industry Robots:
169

Con’t…
4. In Military and Police corps:
5. Entertainment Robots:
170

Robot hardware
 The success of real robots depends at least as
much on the design of sensors and effectors
that are appropriate for the task.
171
Fig: Robot hardware

Review Question
1. What do you learn in general from this course?
2. What is the importance of NLP?
3. Why NL Understanding is hard?
4. Define the following terms
- lexical selection
- speech recognition
172

About Your feeling
1. What do you feel about the course?
2. How do you see the teaching learning process?
3. Do you have any comment/suggestion?
173

introduction to Artificial Intelligence for computer science

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