702: ARTIFICIAL INTELLIGENCE

vinegarclothAI and Robotics

Jul 17, 2012 (4 years and 5 months ago)

448 views

702: ARTIFICIAL INTELLIGENCE

Teaching Scheme
Examination Scheme
Theory
Practical
Sem End
Lect
Prac
Total
Int
Ass
Marks
Hrs
Total
Int Ass
Sem
End
Total
Grand
Total
3
2
5
30
70
3
100
25
25
50
150


Overview of Artificial Intelligence:
What is AI, Importance of AI, Fields of AI.

Problems and Problem Spaces & Search:
AI Problems, Underlying Assumptions, The Level Of the Model, Criteria for success, Defining the
Problem as State Space Search, Production Systems, Problem Characteristics, Issues in the
Design of Search Programs, Problems.

Heuristic Search Techniques:
Generate & Test, Hill Climbing, Best First Search, Problem Reduction, Constraint Satisfaction,
Means-Ends Analysis.

Knowledge Representation:
Knowledge Representation Issues, Predicate Logic, Representing Facts in Logic, Isa – Instance
Relation, Computable functions and predicates, Resolution, Natural Deduction.

Languages for AI Problems:
Prolog, LISP

Symbolic Reasoning Under Uncertainty:
Nonmonotonic Reasoning, Logics for Nonmonotonic Reasoning, Implementation Issues.


Probabilistic Reasoning:
Probability and Bayes Theorem, Certainty Factors and Rule Based System, Bayesian Network,
Dempster-Safer Theory, Fuzzy Logic.

Structured Knowledge:
Semantic Nets, Frames, Conceptual Dependency and Scripts.

Expert System Architecture & Tools:
Introduction, Rule Based System Architecture, Nonoproduction System Architecture, Dealing with
Uncertainty, Knowledge Acquisition & Validation, System Building Tools.

Reference Books:

1. Principles Of Artificial Intelligence:
By N.J.Nilsson, Kaufmann.
2. Artificial Intelligence:
By Rich E. & Kevin Knight ,Tata McGraw Hill.
3. Introduction To AI and Expert Systems:
By Dan .W. Patterson , Prentice Hall India
4. Introduction To AI:
By Charmiak and M.Dermalt , Addision-Wesley.
5. The Engineering of Knowledge Based Systems Theory and Practice:
By A.J.Gongalez & D.D. Dankel , Prentice Hall.
6. Fundamentals of Artificial Neural Networks:
By Mohamad H. Hassoun