Introduction

to
Artificial Intelligence

Dr. Pulak Sahoo

Associate Professor
Silicon Institute of Technology
 Syllabus

Module I
• Artificial Intelligence: Introduction, Four approaches of AI, Foundation &
History of AI
• Intelligent Agents: Int. Agents, PEAS Description, Structure of Agents, Types
of Agents
• Problem Solving by Searching - Problem-Solving Agents, Example Problems,
Searching for Solutions, Uninformed search strategies (BFS, DFS, UCS, DLS,
IDS, BD Search)

Module II
• Informed Search & Exploration: Evaluation & Heuristic functions, Informed
search strategies (Greedy Best First Search, A* search)
• Constraint Satisfaction Problems: Introduction, Map Coloring problem,
Backtracking search for CSPs
• Adversarial Search: Games playing, MiniMax Search, Alpha-Beta Pruning;
• Knowledge & Planning: Knowledge-Based Agents, Planning, PoP. HTN 2
 Syllabus
Module III
• Knowledge and Logic: Intro, Propositional Logic, Inference Rules, CNF,
Examples
• First-Order Logic: First-Order-Predicate Logic & Examples
• Inference in First-Order Logic: Forward Chaining, Backward Chaining,
Resolution & Examples

Module IV
• Uncertain Knowledge & Reasoning: Introduction & approaches, Probability
theory (Axioms of probability, Conditional probabilities with examples), Probability
theory (Bayes’ Rule/Theorem and it's use), Bayesian networks & Burglar alarm
example

Module V
• Learning: Introduction, Four elements of learning agent, Paradigms of learning,
Supervised & Unsupervised Learning, Inductive Learning, Classification Rules
• Decision Tree Induction: Attribute Selection Measures, Neural Networks
(Perceptron), AND, OR & XOR gate examples, Reinforcement Learning
3
 Books

1. Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern

Approach, 2nd Edition, Pearson Education.

2. Elaine Rich, Kevin Knight, Shivshankar B Nair, Artificial Intelligence,

McGraw Hill, 3rd Edition.

3. Nills J. Nilson, “Artificial Intelligence: A New Synthesis”, 2nd Edition,

2000, Elsevier India Publications, New Delhi.

4. Michael Negnevitsky, “Artificial Intelligence: A Guide to Intelligent

Systems”, Second Edition, 2005, Pearson Education, Inc. New Delhi.

5. Dan W. Patterson, “Introduction to Artificial Intelligence and Expert

Systems”, 1st Edition, 1996, PHI Learning Pvt. Ltd., New Delhi.

6. E. Charniak and D. McDermott, Introduction to AI, 1st Edition, Addison-

Wesley, 1985
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 Introduction to AI - Topics

• What is AI

• Four Approaches to AI
 Acting Humanly
 Thinking Humanly
 Acting Rationally
 Thinking Rationally

• Foundations of AI

• History of AI

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What is AI?

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 What is AI?

• Artificial intelligence is an area of computer science

 that emphasizes the creation of intelligent machines
 that think, work & react like humans

• Computers with artificial intelligence can do activities like:

 Speech recognition
 Car responding to master’s voice
 Learning
 Amazon Alexa
 Planning
 Decision making by a Robot
 Problem solving
 Tic-tac-toe, TSP or n-Queen problem 7
 Four Approaches to AI

• Computers with AI can :

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 Four Approaches to AI

• (1) Think humanly

 AI application thinks like a human

 For this it needs to know “working of human mind”

 The study of how we do, what we do (Ex: Car driving)

 How to make the computer do it in the same way

 3 ways to do this:

(1) By Introspection – catching your “own thoughts”

(2) By Psychological experiments – observing a “person’s action”
(3) By Brain imaging – observing “brains in action”

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• (2) Act humanly - Turing Test approach

 Turing Test (1950) of a computer with AI (by Alan Turing)

• The computer & a human are interrogated by another human

behind a barrier via written questions

• Computer passes the test if the human cannot tell if the written
response is from a computer or human

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A Computer with AI needs to have below capabilities :

 Natural Language Processing –to communicate in English

 Knowledge Representation –to store what it knows or hears

 Automated Reasoning – to use the stored info to answer questions

 Machine Learning – to adapt to new/changing circumstances

To pass the Total Turing Test, a machine needs :

 Computer vision – to perceive objects (sensor)

 Robotics – to manipulate objects (actuator)

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• (3) Think Rationally

 Rational thinking – “Right Thinking” with unquestionable reasoning or Logic

 A program can solve any solvable problem described in “logical

notation” (Ex: TSP, 8-Puzzle, Chess…)
 Ex - Socrates is a man; All men are moral; Socrates is moral

 Two obstacles to this approach –

(1) Not easy to represent all problems in logical notations

(2) Big difference between solving a problem “in principle” than “in practice”
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• (4) Act Rationally – The “Rational Agent” approach

 Agent – “One that Acts” – It perceives the env. through sensors & acts on
the env. through actuators (Ex: Robot, Auto-pilot, Vacuum cleaner…)

 Rational Agent – The Agent that acts to achieve the “best outcome”
 In case of uncertainty, the “best expected outcome”

 All the skills needed for Turing test also applies here

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 Foundations of AI
• Following disciplines have contributed to ideas, viewpoints &
techniques to AI

 Philosophy
 Made AI conceivable with the idea that “Mind is in someway like a machine”
 It operates based on “knowledge”
 “Thought” can be used to choose “action”

 Mathematics
 Provided “tools” to represent & manipulate logically certain or uncertain
statements
 Provided algorithms & computations

 Economics
 Helped in making decision that Maximize the expected outcome
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 Foundations of AI

 Neuroscience
 Provided the knowledge about “how brain works”
 How brain is similar & different from computers

 Psychology
 Provided the idea that humans & animals can be considered as “info.
processing machines” 15
 Foundations of AI

 Computer Engg.
 Provided highly efficient & powerful machines to “implement AI
applications”

 Control theory and cybernetics

 Helped in designing devices that act optimally based on “feedback from
the env.”

 Linguistics
 Provided the idea that language for communication can fit into AI
models
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 History of AI

Gestation of AI (1943-55)

• McCulloch & Pitt propose AI based on knowledge of physiology & neurons in brain
• Russel proposed formal analysis
•Turing proposed theory of computation
• Hebb (1949) proposed Hebbian learning on connection between neurons

Birth of AI (1956)

• John McCarthy (Dartmouth College) – official birth place of AI – 2 month workshop

• Newell & Simon – Created a reasoning program “Logic Theorist”

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 History of AI

Early enthusiasm, Great Expectations (1952-69)

• Early success but in limited way by Newell & Simons

• General Problem Solver (GPS) – program designed to imitate human problem-solving
• Physical symbol system hypothesis by Newell & Simons
• Lisp AI programming language by McCarthy
• Neural Network – adalines & perceptrons – by Widrow & Rosenblatt

A dose of reality (1966-73)

• Simons prediction of extraordinary success could not be achieved

• Computer chess champion & theorem proving by computer took 40 years than
prediction of 10 years
• A number of difficulties were encountered while implementing complicated AI
applications

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 History of AI

Knowledge-based systems (1969-79)

• General purpose applications (called weak methods) were unable to handle complex
problems
• Powerful applications using domain-specific knowledge like DENDRAL were needed

AI becomes an Industry (1980-present)

• R.I – 1st commercial Expert System started in 1982 – saved $40m per year for the org
• DEC’s AI group installed 40 E.S.s
• 5th Generation project (10 yrs) of Intelligent AI system – started in Japan using prolog

The return of neural networks (1986-present)

• Back-Propagation – reinvented in 1980’s by 4 different books

• Parallel Distributed processing was introduced
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 History of AI

AI adopts the scientific method (1987-present)

• Revolution in both content & methodology of AI applications
• Hidden Markov model (HMMs) – Dominates AI applications
• Data-Mining technology – spread into AI area
• Bayesian network – dominates uncertain reasoning AI & ES

Emergence of Intelligent Agents (1995-present)

• Intelligent Agents architecture by Newell, Laird & Rosenbloom
• Internet – Most important Intelligent agent env.
• AI systems have become common in Web-based applications
• Human-level AI (HLAI) – Machines that think

Availability of very large data sets (2001-present)

•In many AI applications “Data” is more important than “Algorithm”
•In recent times, availability of large data sources have improved AI applications
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• Ex: Images from web, Genometic sequences, words of English
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