This document provides an introduction and syllabus for an Artificial Intelligence course. The syllabus covers 5 units: introduction to AI and search techniques, optimization problems and search algorithms, knowledge representation structures and logic, uncertain knowledge and reasoning, and game playing techniques and applications to robotics. The course aims to develop an understanding of key concepts in AI like search, knowledge representation, reasoning, and applications. It will examine techniques such as production systems, logic programming, Bayesian networks, and game theory algorithms. Students will learn how to represent and reason with knowledge under uncertainty.

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CS3EA01
ARTIFICIAL INTELLIGENCE
Introduction
Dr Suresh Jain,
Medi-Caps University, Indore

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CS3EA01 Artificial Intelligence (3 0 0)
 Unit I: Introduction to artificial intelligence, various types of
production systems, Characteristics of production systems, Study and
comparison of breadth first search and depth first search techniques.
 Unit II: Optimization Problems: Hill-climbing search Simulated
annealing like hill Climbing, Best first Search. A* algorithm, AO*
algorithms etc, and various types of control strategies, Heuristic
Functions, Constraint Satisfaction Problem.
 Unit III: Knowledge Representation, structures, Predicate Logic,
Resolution, Refutation, Deduction, Theorem proving,
Inferencing,Semantic networks, Scripts, Schemas, Frames,
Conceptual dependency.
Syllabus

3.  Unit IV: Uncertain Knowledge and Reasoning, forward and backward
reasoning, monotonic and nonmonotonic reasoning, Probabilistic
reasoning, Baye’s theorem, Decision Tree, Understanding, Common
sense, Planning.
 Unit V: Game playing techniques like minimax procedure, alpha-beta cut-
offs etc, Study of the block world problem in robotics.
Text Book:
1. Elaine Rich, Kevin Knight and Nair, Artificial Intelligence, TMH
2. S. Russel, Peter Norvig, Artificial Intelligence: A Modern Approach,
Pearson.
Reference Books:
1. Saroj Kausik, Artificial Intelligence, Cengage Learning 4
2. Padhy, Artificial Intelligence and Intelligent Systems, Oxforfd University Press,
3. Nils Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann.
4. David Poole, Alan Mackworth, Artificial Intelligence: Foundations for
Computational Agents, Cambridge Univ. Press..
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Outline
 What is AI?
 Subjects covered in the course
 Requirements
 Textbooks
 Other practical information

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What is AI?
General definition:
AI is the branch of computer science that is concerned with the
automation of intelligent behavior.
It concerned with study and creation of computer systems that
exhibits some form of intelligence.
 what is intelligent behavior?
 is intelligent behavior the same for a computer and a human?

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What is AI?
 at least we have experience with human intelligence
possible definition: intelligence is the ability to form plans to achieve
goals by interacting with an information-rich environment
Tighter definition:
AI is the science of making machines do things that would
require intelligence if done by people. (Minsky)

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Some more definitions of AI
Systems that think like humans
The automation of activities that we associate with human
thinking activities such as decision making, problem solving.
[Bellman 1978]
Systems that think rationally
The study of mental faculties through the use of computational
model. [Charniak and McDermott 1985]
Systems that think, act like humans
The study of how to make computers do things at which, at the
moment people do better.
[Rich & Knight 1991]

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Some more definitions of AI…
Systems that act rationally
AI is concerned with intelligent behavior in artifacts.
[Nelson 1998]

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What is AI?
Intelligence encompasses abilities such as:
 understanding language
 Perception: perceive and comprehend a visual scene
 learning
 reasoning

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Intelligence
• Mental ability
• Ability to acquire, understand and apply knowledge
• Ability to exercise thoughts and reason
• Food of intelligence is knowledge

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Self-defeating definition:
AI is the science of automating intelligent behaviors currently
achievable by humans only.
 this is a common perception by the general public
 as each problem is solved, the mystery goes away and it's no longer
"AI"
successes go away, leaving only unsolved problems
What is AI?

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 AI ranges across many disciplines
computer science, engineering, cognitive science, logic, …
 research often defies classification, requires a broad context
Self-fulfilling definition:
AI is the collection of problems and methodologies studied by
AI researchers.
What is AI?

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Does numeric computation require
intelligence ?
 For humans? Xcalc
3921 , 56
x 73 , 13
286 783 , 68
 For computers?
 Also in the year 1900 ?
 When do we consider a program ‘intelligent’?

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Can we build systems which exhibit these
characteristics?
Achievements in AI
• Systems which can learn from examples, from being told,
from past related experience
In 1959, Arthur Samuel created a computer program that could play
checkers to a high-level using minimax and alpha-beta pruning.
• Systems can solve complex mathematical problems
• Diagnose medical diseases: MYCIN
• Can understand large part of natural language
• System can recognize objects from photographs, video
camera and other sensors
• System which can reason with incomplete and uncertain
facts

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MYCIN Expert System – The First AI
Medical Diagnosis
When was MYCIN Expert System invented?
MYCIN was invented in 1972 when Edward Shortliffe developed the system with a
team from Stanford University.
What is MYCIN Expert System?
MYCIN was designed to help identify bacteria that cause blood infections and other
severe infections like meningitis.
What is the MYCIN system?
The MYCIN System was a computer-based system physicians used to identify blood
infections and the most appropriate treatments.
How does the MYCIN expert system work?
The MYCIN Expert System used backward chaining technology to diagnose
infections based on symptoms and medical history and recommend treatment
based on the data received.
What does MYCIN mean?
MYCIN refers to a backward chaining expert system that helped diagnose and
suggest infections, named after a typical class of antibiotics in use.

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Spite of these achievements, we have not been able to
produce coordinated, autonomous systems which
posses some of the basic abilities of a 3 year old child
• Ability to recognize and remember variety of objects in a
scene
• To learn new sound and associate them with objects and
concepts
• To adopt to many diverse new situations

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AI is not
• The study and creation of conventional computer systems
• The study of mind (psychology), nor the body (physiology),
nor the language (linguistics/cognitive science)
AI Goal
Develop working systems that are truly capable of performing
tasks that require high level of intelligence.

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Component areas of study
• Robotics
• Knowledge representation, storage and recall
• Learning models
• Inference techniques
• Commonsense reasoning
• Dealing with uncertainty in reasoning and decision making
• Understanding natural language
• Pattern recognition and machine vision methods
• Speech recognition and synthesis
• Variety of AI tools

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AI success
• 5th generation robots (Japanese program)
• Autonomous Land Vehicle (ALU): driverless military
vehicle (USA)
• Pilots associates ( expert system to assist fighter pilots
(Singapore)

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Significant AI Events
1958: LISP (John McCarthy)
1961-65: Samuel developed a program which learned to play
checkers at a master's level
1965: Robinson introduced Resolution Method (inference
method) in logic
1965: Work on DENDRAL begun at Stanford university.
Expert system which discovered molecular structures
given with information of the constituents of the compound
and mass spectra data
GPS: General problem Solver (Newell, Shaw Simon)

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Task Domain of AI
Mundane task
Perception (vision, speech), Natural language
understanding, Commonsense reasoning
Formal task
Game (Chess, Checkers)
Mathematics (geometry, logic)
Expert task
Engineering ( design, fault finding, manufacturing
planning)
Scientific analysis, medical diagnosis, financial analysis,
chemical analysis, scientific discovery)

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To situate the question:
Two different aims of AI:
 Long term aim:
 develop systems that achieve a level of ‘intelligence’ similar /
comparable / better? than that of humans.
 not achievable in the next 20 to 30 years
 Short term aim:
 on specific tasks that seem to require intelligence: develop
systems that achieve a level of ‘intelligence’ similar /
comparable / better? than that of humans.
 achieved for very many tasks already

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The long
term goal:
The Turing
Test

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Reproduction versus Simulation
 At the very least in the context of the short term aim
of AI:
 we do not want to SIMULATE human intelligence
BUT:
 REPRODUCE the effect of intelligence
Nice analogy with flying !

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Artificial Intelligence versus Natural Flight

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Which applications are easy ?
 For very specialized, specific tasks: AI
Example:
ECG-diagnosis
 For tasks requiring common sense: AI

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Modeling Knowledge …
and managing it .
The LENAT experiment:
15 years of work by 15 to 30 people, trying to model
the common knowledge in the word !!!!
Knowledge should be learned, not engineered.

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Multi-disciplinary domain:
 Engineering:
 robotics, vision, control-expert systems, biometrics,
 Computer Science:
 AI-languages , knowledge representation, algorithms, …
 Pure Sciences:
 statistics approaches, neural nets, fuzzy logic, …
 Linguistics:
 computational linguistics, phonetics en speech, …
 Psychology:
 cognitive models, knowledge-extraction from experts, …
 Medicine:
 human neural models, neuro-science,...

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Artificial Intelligence is ...
 In Engineering and Computer Science:
 The development and the study of advanced
computer applications, aimed at solving tasks that -
for the moment - are still better preformed by humans.
 Notice: temporal dependency !
 Ex. : Prolog

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Pre-history of AI
birth of AI occurred when Marvin Minsky & John McCarthy
organized the Dartmouth Conference in 1956
 brought together researchers interested in "intelligent machines"
 for next 20 years, virtually all advances in AI were by attendees
 Minsky (MIT), McCarthy (MIT/Stanford), Newell & Simon (Carnegie),…
John McCarthy
Marvin Minsky

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SHRLDU has just completed the command:
“Find a block which is taller than the one you are holding and put on the box”
Example of microworld.
https://hci.stanford.edu/winograd/shrdlu/
SHRLDU

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Eliza sample dialog
 HUMAN: You are like my father in some ways.
 ELIZA: What resemblance do you see?
 HUMAN: You are not very aggressive, but I think you don’t want me
to notice that.
 ELIZA: What makes you think I am not very aggressive?
 HUMAN: You don’t argue with me.
 ELIZA: Why do you think I don’t argue with you?
 HUMAN: You are afraid of me.
 ELIZA: Does it please you to believe I am afraid of you?
 HUMAN: My father is afraid of everybody.
 ELIZA: What else comes to mind when you think of your father?
 HUMAN: Bullies.
 https://web.njit.edu/~ronkowit/eliza.html

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Neural networks

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Neural networks

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Genetic algorithms

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Genetic algorithms

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Branches of AI
 Games - study of state space search, e.g., chess
 Automated reasoning and theorem proving, e.g., logic
theorist
 Expert/Knowledge-based systems
 Natural language understanding and semantic modeling
 Model human cognitive performance
 Robotics and planning
 Automatic programming
 Learning
 Vision

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CO 4204 ARTIFICIAL INTELLIGENCE
1. Definition of AI
Heuristics, Non-Algorithms, Symbolic Processing, Pattern
Matching, Machine Intelligence & AI, AI techniques. Problems and
problem characteristics, production Systems, knowledge and search,
State-Space.
2. Search Techniques
Any path vs. optional path search strategies, DFS, BFS, Best-first, Hill
climbing, Branch & Board and Dynamic programming, AX algorithm,
Game search.
3. Knowledge and Knowledge Representation
Various scheme of KR – Predicate logic, reasoning, logic
programming, frames, scripts, conceptual dependency, Semantic nets.
Reasoning under uncertainty, Fuzzy reasoning and control.

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Textbooks
Elaine Rich
Kevin Knight
Artificial Intelligence
McGraw-Hill