Subject Code : 410445
Subject Name : Artificial Intelligence
Theory :100 Marks Term Work : 25 Marks Oral: 50 Marks Duration: 3 Mrs
Objectives:
• To understand the concepts of Artificial intelligence
• To learn and Understand the knowledge representation techniques for knowledgebase
• To learn and Understand the fundamentals of Neural Network
UNIT I-
Introduction
Definition, What is A.I? Foundation of A.I., History, intelligent Agents, Agent Architecture, A.I.
Application (E Commerce, & Medicine), A.I. Representation, Properties of internal representation
Futures of A.I, Production System, and Issue in design of search Programs
Logic Programming
Introduction, Logic, Logic Programming, Forward and Backward reasoning , Forward and
Backward chaining rules
8 Hrs
UNIT II:
Heuristic search techniques.
Heuristic search, Hill Climbing, Best first search, mean and end analysis, Constraint Satisfaction,
A* and AO* Algorithm.
Game playing
Minmax search procedure, Alpha beta cutoffs, waiting for Quiescence, Secondary search.
7 Hrs,
UNIT III:
Knowledge Representation
Basic of Knowledge representation, Knowledge representation Paradigrams, Prepositional Logic,
Inference Rules in Prepositional Logic, Knowledge representation using Predicate
logic : Predicate Calculus, Predicate and arguments, ISA hierarchy, Frame notation , Resolution , Natural Dedication
Knowledge representation using non monotonic logic:
TMS (Truth maintenance system), statistical and probabilistic reasoning, fuzzy logic, structure
knowledge representation, semantic net, Frames, Script, Conceptual dependency.
10 Hrs
UNIT IV:
Learning:
What is Learning? Types of Learning (Rote, Direct instruction Analogy, Induction, Deduction)
Planning:
Block world, strips, Implementation using goal stack, Non linear planning with goal stacks,
Hierarchical planning, least commitment strategy.
7 Hrs
UNIT V:
Advance Al Topics
Natural Language Processing
Introduction, Steps in NLP , Syntactic Processing , ATN, RTN, Semantic analysis, Discourse & Pragmatic Processing.
Perception : Perception, Action, Robot Architecture
8 Hrs
UNIT VI:
Neural Networks: Introduction to neural networks and perception-qualitative Analysis. Neural net architecture and applications.
Expert system: Utilization and functionality, architecture of expert system, knowledge representation, two case studies on expert systems.
8 Hrs
Text Books
1. Eugene, Charniak, Drew Mcdermott: "Introduction to artificial intelligence."
2. Eiaine Rich and Kerin Knight: "Artificial Intelligence."
3. Kishen Mehrotra, Sanjay Rawika, K Mohan; "Artificial Neural Network." '-
Reference Book
1. Stuart Russell & Peter Nerving : "Artificial Intelligence : A Modern Approach", Prentice Hall, 2nd Edition.
2. Ivan Bratko : "Prolog Programming For Artificial Intelligence" , 2nd Edition Addison Wesley, 1990.
3.' Herbert A. Simon, "The Sciences of the Artificial ", MITTress, 3rd Edition (2nd Printing),1995.
4. Tim Jones "Artificial Intelligence Application Programming" M. Dreamtech Publication
Laboratory work:
Assignment based on:
1. Implement 8 puzzle problem using A* algorithm • •
2. Implement AO* algorithm for tower of Hanoi .
3. Implementation of Unification Algorithm.
4. Implementation of Truth maintenance system using prolog
5. Implementation of Min/MAX search procedure for game Playing
6. Parsing Method Implementation using Prolog.
7. Development of mini expert system using Prolog / Expert System Shell " Vidwan" Designed By NCST Mumbai.
Staff should frame any six assignments on above topics.
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