Courseware on Artificial Intelligence
Courseware on Artificial Intelligence,
by RC Chakraborty, Visiting Professor JIET Guna
. This "Courseware on AI" refers to the even semester (Jan–April, 2007)
course, title "Artificial Intelligence", Code CI 608 (3-0-2), 4 Credits, Lectures-42 hours, I tau
at JIET Guna. The Pre-requisite, Ob
ectives, Course Outline, Teachin
Scheme, Text Book and References are available at JIET website, URL http://www.jiet.ac.in
The Topics covered in this undergraduate AI course are detailed below. The same course I tau
in the previous year (Jan–April, 2006) also. Since then the lecture slides – total 448 nos, have
gone through one update. Presently, the lecture slides in PDF format are available in JIET LAN
sever. I am striving for making an undergraduate " Courseware on AI" to be available on the
Web, free of charge, to any user anywhere in the world before next session (Jan–April, 2008)
Declaim : My endeavor in making an undergraduate "Courseware on AI", has no commercial
interest. It would be available on the Web, free of charge, to any user anywhere. It is no way
connected with any institutions makin
their course materials available as open educational
resources (OER) like MIT-OpenCourseWare (MIT OCW) or Carnegie Mellon University-Open
Learning Initiative, etc.
Lecture 1, 2, 3, 4, 5, (5hrs)
1. Introduction to AI:
‡ Definitions: Artificial Intelli
Understanding, “Hard” or “Strong” AI, “Soft” or “Weak” AI, Cognitive Science.
‡ Goals of AI.
‡ AI Approaches : Co
nitive science, Laws of thou
‡ AI Techniques : Describe and match, Goal reduction, Constraint satisfaction,
, Generate and test, Rule based systems, Neural Networks,
Genetic Algorithms, Reinforcement learning.
‡ Branches of AI : Lo
ical AI, Search in AI, Pattern Reco
Representation, Inference, Common sense knowledge and reasoning, Learnin
Planning, Epistemology, Ontology, Heuristics, Genetic programming.
‡ Applications of AI : Game playin
, Speech Reco
Language, Computer Vision, Expert Systems.
Lecture 6, 7, 8, 9, (4hrs)
2. Problem Solving, Search and Control Strategies:
‡ General Problem solving : Problem definitions, Problem Space, Problem
solution, Problem description, Structure of the state space, States Chan
‡ Search and Control strategies :Al
orithm’s performance and complexity,
“big-o” notation, tree structure, stacks and queues, search taxonomy, control
strategies, forward chaining, backward chaining.
‡ Exhaustive Searches : Depth first and Breadth first search, Compare Depth-
first and Breadth-first search.
‡ Heuristic Search Techniques: Characteristics, Comparison, Algorithms.
‡ Constraint Satisfaction problems.
Lecture 10, 11, 12, 13, 14, 15, 16, (7 hrs)
3. KR Issues, Predicate Logic, Rules
‡ Knowledge Representation :Introduction, Representations and Mappin
Approaches, Issues, the Frame Problems.
‡ KR Using Predicate Logic : Representing Simple Facts in Logic, Representin
Instance and “Isa” Relationships, Computable Functions and Predicates,
‡ KR Using Rules : Procedural versus Declarative Knowled
Forward versus Backward Reasoning, Matching, Control Knowledge.
Lecture 17, 18, 19, 20, 21, 22, 23, 24 (8hrs)
4. Reasoning System Symbolic , Statistical
‡ Reasoning: Definitions : Reasonin
, Formal and Informal lo
Monotonic and Non-monotonic Logic; Methods of Reasoning–deductive, inductive
, abductive, analogy; Sources of uncertainty, Approaches to reasoning– Symbolic,
Statistical and Fuzzy.
‡ Symbolic Reasoning: Introduction to Non-monotonic Reasoning, Logic for Non-
, Implementation issues, Au
a Problem Solver,
Implementation: Depth-First-Search, Implementation : Breath-First-Search.
‡ Statistical Reasoning : Probability and Bayes’ theorem, Certainty Factors Rule-
Based Systems, Bayesian Networks, Dempster – Shafer Theory, Fuzzy Logic.
Lecture 25, 26, 27, 28, (4hrs)
5. Game Playing
‡ The Mini-Max Search Procedure
‡ Game playing with Mini-Max : Example
Lecture 29, 30, 31, 32, 33, 34, (6hrs)
‡ What is Learning: Definition, Learning agents – components, Paradi
‡ Rote learning :
‡ Learning from Example : Induction, Winston's learning, Version Spaces -
Learning Algorithm (Generalization and Specialization tree), Decision trees - ID3
Explanation Based Learning (EBL) : General approach, EBL Architecture, EBL
System, generalization problem, Explanation structure.
‡ Discovery : Theory driven discovery – AM system, Data driven discovery -
‡ Clustering : Distance functions - Euclidean
eometry, Euclidean distance,
Manhattan distance, Minkowski metric, K-Mean Clustering – algorithm.
‡ Analogy : Problem solving by analogy
‡ Neural net and Genetic learning
Lecture 35, 36, 37, 38, (4hrs)
7. Expert System
‡ Introduction : Expert system components and human interfaces, Expert system
characteristics, Expert system features.
‡ Knowledge Acquisition : Issues, Techniques.
‡ Knowledge Base : Representing and using domain knowledge - IF-THEN rules,
Semantic network, Frames.
‡ Working Memory
‡ Inference Engine : Forward chaining - data driven approach, Backward chainin
- goal driven approach, Tree searches - DFS, BFS.
‡ Expert System Shells : Shell components and description.
‡ Explanations : Example, Types of Explanation
Lecture 39, 40, (4hrs)
8. Natural Language Processing
‡ Introducton : Steps in process, Morpholo
ical analysis, Syntactic analysis,
Semantic analysis, Discourse Integraton, Pragmatic analysis.
‡ Syntactic Processing : Grammars and Parsers – top down and bottom up,
Augmented Transition Nets (ATN), Unification Grammars.
Semantic and Pragmatic Analysis
Lecture 41, 42, (4hrs)
9. Common Sense
‡ Introduction: Common sense Knowled
e and Reasonin
, Common sense
‡ Physical world : Modelin
the Qualitative World, Reasonin
Common sense Ontologies : Time, Space, Material
Memory Organization : Short term memory (STM), Long term memory (LTM)
Course books :
‡ Elaine Rich and Kevin Knight, Carnegie Mellon University, “Artificial Intelligence, 2006
‡ Stuart Russel & Peter. Norvi
, “Artificial Intelli
ence: A Modern Approach”, Prentice Hall,
June 11, 2007 R C Chakraborty