Artificial Intelligence
Course Code: ITT451
By
Ms.C.B.Thaokar
Assistant Professor
Department of Information Technology
RCOEM, Nagpur
Syllabus
Unit I:
Introduction: Introduction, What Is AI?, The Foundations of Artificial Intelligence,
The History of Artificial Intelligence, The State of the Art.
Intelligent Agents: Agents and Environments Good Behavior: The Concept of
Rationality, The Nature of Environments, The Structure of Agents.
Unit II :
Problem-solving: Solving Problems by Searching, Problem-Solving Agents, Example
Problems, Searching for Solutions, Uninformed Search Strategies, Informed (Heuristic)
Search Strategies, Heuristic Functions.
Beyond Classical Search: Local Search Algorithms and Optimization Problems: Hillclimbing search Simulated annealing, Local beam search, Genetic algorithms, Local
Search in Continuous Spaces, Searching with Non-deterministic Actions: AND-OR
search trees, Searching with Partial Observations
Unit III :
Constraint Satisfaction Problems: Defining Constraint Satisfaction Problems,
Constraint Propagation: Inference in CSPs, Backtracking Search for CSPs, Local
Search for CSPs, The Structure of Problems.
Game Playing: Adversarial Search, Games, Optimal Decisions in Games, The
minimax algorithm, Optimal decisions inC.B.Thaokar
multiplayer games, Alpha–Beta Pruning.2
Syllabus
contd.
Unit IV :
Logic and Knowledge Representation: Knowledge-Based Agents, The Wumpus World, Logic,
Propositional Logic: A Very Simple Logic, Propositional Theorem Proving, Effective
Propositional Model Checking, Agents Based on Propositional Logic
First-Order Logic: Representation Revisited Syntax and Semantics of First-Order Logic, Using
First-Order Logic, Knowledge Engineering in First-Order Logic, Inference in First-Order
Logic, Propositional vs. First-Order Inference, Unification and Lifting, Forward Chaining,
Backward Chaining, Resolution.
Unit V :
Natural Language Processing: Introduction, Syntactic Analysis, Semantic Analysis, Discuses
and Pragmatic Processing.
Introduction and Fundamentals of Artificial Neural Networks: Biological prototype,
Artificial Neuron, Single layer Artificial, Neural Networks, Multilayer Artificial Neural
Networks, Training of Artificial Neural Networks.
Unit VI :
Machine Learning: Probability basics - Bayes Rule and its Applications – Bayesian Networks –
Exact and Approximate Inference in Bayesian Networks - Hidden Markov Models - Forms
of Learning - Supervised Learning - Learning Decision Trees – Regression and Classification
with Linear Models - Artificial Neural Networks – Nonparametric Models - Support Vector
Machines - Statistical Learning - Learning with Complete Data - Learning with Hidden
Variables- The EM Algorithm – Reinforcement Learning
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Syllabus
contd.
Text Books
1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern
Approach, Prentice-Hall.
2. David L. Poole, Alan K. Mackworth, Artificial Intelligence: Foundations of
Computational Agents, Cambridge University Press, 2010.
3. Natural Language processing and Information Retrieval: U.S. Tiwary,
Tanveer Siddique, 1st edition, Oxform University Press.
Reference Books
1. Nils J. Nilsson, Artificial Intelligence: A New Sythesis, Morgan-Kaufmann.
2. Ethem Alpaydin, Introduction to Machine Learning (Adaptive
Computation and Machine Learning series), The MIT Press; second
edition, 2009
3. Introduction to Artificial Intelligence & Expert System: D. Patterson 1st
Edition, PHI.
a) NPTEL Videos - Dr. Sudeshna Sarkar
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b) NPTEL Videos - Dr. Deepak Khemani
Course Outcomes
1. Ability to formulate an efficient problem space for a problem
expressed in natural language.
2. Select a search algorithm for a problem and estimate its time
and space complexities.
3. Demonstrate knowledge representation using the appropriate
technique for a given problem.
4. Possess the ability to apply AI techniques to solve problems of
game playing, and machine learning.
5. Understand the concept of Natural Language Processing and
applying the different of machine learning algorithms.
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UNIT-1
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Introduction: what is AI?
History
Application
Intelligent agents
Performance Measures
Rationality
Structure of Agents
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Introduction
• In March 2016, Alpha-Go of DeepMind defeated Lee Sedol,
who was the strongest human GO player at that time.
• Deep Blue IBM machine won its first game against world
champion Garry Kasparov in game one of a six-game match
on 10 February 1996.
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Introduction
 A- NLP engine (like IBM Watson – deep blue-Jeopardy)
In 2011, a Jeopardy! quiz show exhibition match, IBM's
question answering system, Watson, defeated the two greatest
Jeopardy! champions, Brad Rutter and Ken Jennings, by a
significant margin.
Baidu brain AI project of china Baidu, Inc.
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Introduction
• Humanoid Robot Sophia: Sophia is a social humanoid robot
developed by Hong Kong-based company Hanson Robotics.
Sophia, the robot became the first robot to receive citizenship
of any country
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Definitions
• AI is a branch of computer science concerned with the study
and creation of computer systems that exhibits some form of
intelligence.
• Systems that can learn new concepts and tasks.
• Systems that can understand a Natural Langauge or perceive
and comprehend a visual scene
• System that perform other types of feats that require human
type of intelligence.
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What AI is not?
• AI is not the study and creation of conventional computer
systems.
• Its not the study of the mind , nor of the body, nor of langauges
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Obvious question
• What is AI?




Programs that behave externally like humans?
Programs that operate internally as humans do?
Computational systems that behave intelligently?
Rational behaviour?
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What is Intelligence?
Intelligence is a property of mind that encompasses many
related mental abilities, such as the capabilities to
–
reason
–
plan
–
solve problems
–
think abstractly
–
understand ideas and language
–
learn
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What’s involved in Intelligence?
• Ability to interact with the real world
–
–
–
–
to perceive, understand, and act
e.g., speech recognition and understanding and synthesis
e.g., image understanding
e.g., ability to take actions, have an effect
• Reasoning and Planning
– modeling the external world, given input
– solving new problems, planning, and making decisions
– ability to deal with unexpected problems, uncertainties
• Learning and Adaptation
– we are continuously learning and adapting
– our internal models are always being “updated”
• e.g., a baby learning to categorize and recognize animals
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Turing Test
• Human beings are intelligent
• To be called intelligent, a
machine must produce responses
that are indistinguishable from
those of a human
Alan Turing
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Turing Test
AI system
Experimenter
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Control
Foundation of AI
•
•
•
•
•
•
•
Philosophy
Mathematics
Economics
Neuroscience
Psychology
Control Theory
John McCarthy- coined the term- 1950’s
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History of AI
1943: early beginnings
McCulloch & Pitts: Boolean circuit model of brain
1950: Turing
Turing's "Computing Machinery and Intelligence“
1956: birth of AI
Dartmouth meeting: "Artificial Intelligence“ name adopted
1950s: initial promise
Early AI programs, including
Samuel's checkers program
Newell & Simon's Logic Theorist
1955-65: “great enthusiasm”
Newell and Simon: GPS, general problem solver
Gelertner: Geometry Theorem Prover
McCarthy: invention of LISP
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History of AI
1966—73: Reality dawns
Realization that many AI problems are intractable
Limitations of existing neural network methods identified
Neural network research almost disappears
1969—85: Adding domain knowledge
Development of knowledge-based systems
Success of rule-based expert systems,
E.g., DENDRAL, MYCIN
But were brittle and did not scale well in practice
1986-- Rise of machine learning
Neural networks return to popularity
Major advances in machine learning algorithms and applications
1990-- Role of uncertainty
Bayesian networks as a knowledge representation framework
1995-- AI as Science
Integration of learning, reasoning, knowledge representation
AI methods used in vision, language, data mining, etc
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Categories of AI System
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Categories of AI System
• Systems that think like humans
• Systems that act like humans
• Systems that think rationally
• Systems that act rationally
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Categories of AI System
1. Systems that think like humans
 Most of the time it is a black box where we are not clear
about our thought process.
 One has to know functioning of brain and its mechanism
for possessing information.
 Neural network is a computing model for processing
information similar to brain.
The exciting new effort to make computers think . . . machines with minds, in
the full and literal sense.” (Haugeland, 1985)
“The automation of activities that we associate with human thinking, activities
such as decision-making, problem solving, learning . . .”
(Bellman, 1978)
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Categories of AI System
2. Systems that act like humans
 The overall behavior of the system should be human like.
 It could be achieved by observation.
The art of creating machines that perform functions that require intelligence
when performed by people.” (Kurzweil, 1990)
“The study of how to make computers do things at which, at the moment,
people are better.” (Rich and Knight, 1991)
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Categories of AI System
3. Systems that think rationally
 Such systems rely on logic rather than human to measure
correctness.
 For thinking rationally or logically, logic formulas and
theories are used for synthesizing outcomes.
The study of mental faculties through the use of computational models.”
(Charniak and McDermott, 1985)
“The study of the computations that make it possible to perceive, reason, and act.”
(Winston, 1992)
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Categories of AI System
4. Systems that act rationally
 Rational behavior means doing right thing.
 Even if method is illogical, the observed behavior
must be rational (logical).
Computational Intelligence is the study of the design of intelligent agents.”
(Poole et al., 1998)
“AI . . . is concerned with intelligent behavior in artifacts.” (Nilsson, 1998)
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Intelligent Systems in Your Everyday Life
• Post Office
– automatic address recognition and sorting of mail
• Banks
– automatic check readers, signature verification systems
– automated loan application classification
• Customer Service
– automatic voice recognition
• The Web
– Identifying your age, gender, location, from your Web surfing
– Automated fraud detection
• Digital Cameras
– Automated face detection and focusing
• Computer Games
– Intelligent characters/agents
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AI have applications?
• Autonomous planning and scheduling of tasks aboard a
spacecraft
• Steering a driver-less car
• Understanding language
• Robotic assistants in surgery
• Monitoring trade in the stock market to see if insider trading is
going on
• Automated Reasoning and Theorem Proving
• Expert Systems
• Natural Language Understanding and Semantic Modelling
• Modelling Human Performance
• Planning and Robotics
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Intelligent Agents
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Agents
• An agent is anything that can be viewed as perceiving its
environment through sensors and acting upon that
environment through actuators
• Human agent: eyes, ears, and other organs for sensors
• Hands, legs, mouth, and other body parts for actuators
• Robotic agent: cameras and infrared range finders for
sensors
• various motors for actuators
Agents and environments
• The agent function maps from percept histories to
actions:
[f: P*  A]
• The agent program runs on the physical architecture to
produce f
• agent = architecture + program
Ex-1:Vacuum-cleaner world
• Percepts: location and contents, e.g., [A,Dirty]
• Actions: Left, Right, Suck, NoOp
Ex-1:Vacuum-cleaner world
Program implements the agent function
tabulated in Fig. 2.3
Function Reflex-Vacuum-Agent([location,status]) return an
action
If status = Dirty then return Suck
else if location = A then return Right
else if location = B then return left
Rational agents
• 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
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.
Concept of Rationality
Rational agent
One that does the right thing
= every entry in the table for the agent function
is correct (rational).
What is correct?
The actions that cause the agent to be most
successful
So we need ways to measure success.
Rationality factors PEAS
• PEAS:
Performance
Actuators, Sensors
measure,
Environment,
• Must first specify the setting for intelligent agent
design
–
–
–
–
Performance measure
Environment ( Prior Knowledge)
Actuators ( Sequence of actions)
Sensors ( Percept sequence )
PEAS for Task environments
• Consider, e.g., the task of designing an automated taxi driver:
– Performance measure:
• Safe, fast, legal, comfortable trip, maximize profits
– Environment:
• Roads, other traffic, pedestrians, customers
– Actuators:
• Steering wheel, accelerator, brake, signal, horn
– Sensors:
• Cameras, sonar, speedometer, GPS, odometer, engine
sensors, keyboard
Task environments
A sketch of automated taxi driver
PEAS
Agent: Medical diagnosis system
• Performance measure:
– Healthy patient, minimize costs, lawsuits
• Environment:
– Patient, hospital, staff
• Actuators:
– Screen display (questions, tests, diagnoses, treatments,
referrals)
• Sensors:
– Keyboard (entry of symptoms, findings, patient's answers)
PEAS
Agent: Part-picking robot
• Performance measure:
– Percentage of parts in correct bins
• Environment:
– Conveyor belt with parts, bins
• Actuators:
– Jointed arm and hand
• Sensors:
– Camera, joint angle sensors
PEAS
Agent: Interactive English tutor
• Performance measure:
– Maximize student's score on test
• Environment:
– Set of students
• Actuators:
– Screen display (exercises, suggestions, corrections)
• Sensors:
– Keyboard
Properties of task environments
Fully observable vs. Partially observable
 If an agent’s sensors give it access to the complete
state of the environment at each point in time then
the environment is effectively and fully observable
if the sensors detect all aspects
That are relevant to the choice of action
 Partially observable
•
An environment might be Partially observable because of noisy and
inaccurate sensors or because parts of the state are simply missing from
the sensor data.
• Example:
 A local dirt sensor of the cleaner cannot tell whether other squares are
clean or not
Properties of task environments
 Deterministic vs. stochastic
 next state of the environment completely determined by the
current state and the actions executed by the agent, then the
environment is deterministic
 Stochastic means the next state has some uncertainty associated
with it. Uncertainty could come from randomness, lack of a
good environment model, or lack of complete sensor coverage.
Outcome cannot be determined
Eg. Taxi driving is stochastic because of some unobservable
aspects
Properties of task environments
 Episodic vs. sequential
 An agents experience is divided into atomic episodes .
In each episode agent receives percept and performs single
operation .The quality of the agent’s action does not depend
on other episodes
 Every episode is independent of each other
 Episodic environment is simpler
 The agent does not need to think ahead
 Sequential
 Current action may affect all future decisions
-Ex. Taxi driving and chess.
Properties of task environments
 Static vs. dynamic
An environment that keeps constantly changing itself when the
agent is up with some action is said to be dynamic.
E.g., the number of people on the street
 Other agents in an environment make it dynamic
An idle environment with no change in its state is called a
static environment. Agent need not look at the world to take
actions
E.g., Chess without clock
 Semidynamic
 environment is not changed over time
 but the agent’s performance score does change
E.g., Chess with clock
Properties of task environments
 Discrete vs. continuous
 If there are a limited number of distinct states, clearly
defined percepts and actions, the environment is discrete
 E.g., Chess game
Continuous: It relies on unknown and rapidly changing
data sources.
E.g Vision systems in drones
Self-driving cars
Properties of task environments
 Single agent VS. multiagent
 Playing a crossword puzzle – single agent
 Chess playing – two agents
 Competitive multiagent environment
Chess playing
 Cooperative multiagent environment
Automated taxi driver
Properties of task environments
Known vs. unknown
This distinction refers not to the environment itself but to the
agent’s (or designer’s) state of knowledge about the
environment.
- In known environment, the outcomes for all actions are given.
( example: solitaire card games rules are known but Partially
observable).
- If the environment is unknown, the agent will have to learn
how it works in order to make good decisions.
( example: new video game).
Environment Examples
Environment
Chess with a clock
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Obser
vable
Determi
nistic
Episodic
Static
Discrete
Agents
Environment Examples
Environment
Obser
vable
Determi
nistic
Episodic
Static
Discrete
Agents
Chess with a clock
Fully
Strategic
Sequential
Semi
Discrete
Multi
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Examples
Environment
Obse Deter Episodic Stat Discre
rvabl ministi
ic
te
e
c
Agent
s
Chess with a
clock
Fully
Strateg Sequent Stat
ic
ial
ic
Discre
te
Multi
Taxi driving
Parti
al
Stocha Sequent Dyn
stic
ial
ami
c
Contin Multi
uous
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Examples
Environment
Obse
rvabl
e
Deter
ministi
c
Chess with a clock
Fully
Strateg Sequenti Sem Discret Multi
ic
al
i
e
Taxi driving
Parti
al
Stocha
stic
Medical diagnosis
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Episodic
Stati Discret Agent
c
e
s
Sequenti Dyn Contin
al
amic uous
Multi
Environment Examples
Environment
Obse Deter Episodic Stat Discre
rvabl ministi
ic
te
e
c
Agent
s
Chess with a
clock
Fully
Strateg Sequent Sem Discre
ic
ial
i
te
Multi
Taxi driving
Parti
al
Stocha Sequent Dyn
stic
ial
ami
c
Contin Multi
uous
Medical
diagnosis
Parti
al
Stocha Episodic Stat
stic
ic
Contin Single
uous
Fully observable vs. partially observable
Deterministic vs. stochastic / strategic
Episodic vs. sequential
Static vs. dynamic
Discrete vs. continuous
Single agent vs. multiagent
Environment Examples
Environment
Obse Deter Episodic Stat Discre
rvabl ministi
ic
te
e
c
Agent
s
Chess with a
clock
Fully
Strateg Sequent Sem Discre
ic
ial
i
te
Multi
Chess without a
clock
Fully
Strateg Sequent Stat
ic
ial
ic
Discre
te
Multi
Taxi driving
Parti
al
Stocha Sequent Dyn
stic
ial
ami
c
Contin Multi
uous
Medical
Deterministic vs.
diagnosis
stochastic / strategic
Robot part
Episodic vs. sequential
picking
Static vs. dynamic
Parti
al
Stocha Episodic Stat
stic
ic
Contin Single
uous
Fully
Deter Episodic Sem Discre
ministi
i
te
c
Fully observable vs.
partially observable
Discrete vs.
continuous
Single agent vs.
multiagent
Single
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
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)
Agent programs
Skeleton design of an agent program
Agent types / Agent programs
Four basic types in order of increasing generality:
• Simple reflex agents
• Model-based reflex agents
• Goal-based agents
• Utility-based agents
• Learning agents
Simple reflex agents
It selects action on the basis of current percept and does not
remember percept history.
Eg. Vaccum cleaner agent (Local )
It uses just condition-action rules
 The rules are like the form “if … then …”
 Work comfortably only
if the environment is fully observable
If environment is partially observable than infinite loops
are unavoidable
E.G. Vaccum cleaner location sensor
Simple reflex agents
Simple reflex agents
Simple reflex agents
Problems with Simple reflex agents are :
•Very limited intelligence.
•No knowledge of non-perceptual parts of state.
•Usually too big to generate and store.
•If there occurs any change in the environment,
then the collection of rules need to be updated.
Model-based Reflex Agents
For the world that is partially observable
the agent has to keep track of an internal state
That depends on the percept history
The current state is stored inside the agent which
maintains some kind of structure describing the part
of the world which cannot be seen.
E.g., driving a car and changing lane
Requiring two types of knowledge
How the world evolves independently of the agent
How the agent’s actions affect the world
Model-based Reflex Agents
Model-based Reflex Agents
The agent is with memory
Goal-based agents
These agents take decision based on how far they are currently
from their goal (description of desirable situations).
Action is intended to reduce its distance from the goal.
The knowledge that supports its decisions is represented
explicitly and can be modified, which makes these agents more
flexible.
They usually require search and planning.
The goal-based agent’s behavior can easily be changed.
Goal-based agents
Utility-based agents
The agents which are developed having their end uses as
building blocks are called utility based agents.
When there are multiple possible alternatives, then to
decide which one is best, utility-based agents are used.
Achieving the desired goal is not enough.
Look for a quicker, safer, cheaper trip to reach a
destination. i.e agent happiness should be taken into
consideration.
Because of the uncertainty in the world, a utility agent
chooses the action that maximizes the expected utility that
describes degree of happiness.
E.g. Food prepared in the canteen is outcome but whether its
good / bad
Utility-based agents
Learning Agents
After an agent is programmed, can it work
immediately?
No, it still need teaching
In AI,
Once an agent is done
We teach it by giving it a set of examples
Test it by using another set of examples
Learning Agents