Artificial Intelligence

Paper Code: 
MCA 425E
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 
  • To provide background in the field of artificial intelligence.
  • To provide the specific ideas about modeling and analytical skills (e.g., search, logic, probability), knowledge of many of the most important knowledge representation, reasoning, and machine learning schemes, and a general understanding of AI principles and practice.
  • To prepare the student for further study of AI, as well as to inform any work involving the design of computer programs for substantial application domains.
12.00
Unit I: 
General Issues and overview of AI

The AI problems: what is an AI technique, Characteristics of AI applications Problem Solving, Search and Control Strategies General Problem solving, Production systems, Control strategies, forward and backward chaining Exhaustive searches: Depth first Breadth first search.

12.00
Unit II: 
Heuristic Search Techniques

Hill climbing, Branch and Bound technique, Best first search and A* algorithm, AND/OR Graphs, Problem reduction and AO* algorithm, Constraint Satisfaction problems Game Playing Min Max Search procedure, Alpha-Beta cutoff, Additional Refinements.

12.00
Unit III: 
Knowledge Representation

First Order Predicate Calculus, Resolution Principle and Unification, Inference Mechanisms Horn’s Clauses, Semantic Networks, Frame Systems and Value Inheritance, Scripts, Conceptual Dependency AI Programming Languages Introduction to LISP, Syntax and Numeric Function, List manipulation functions, Iteration and Recursion, Property list and Arrays, Introduction to PROLOG.

12.00
Unit IV: 
Natural Language Processing and Parsing Techniques

Context-Free Grammar, Recursive Transition Nets (RTN), Augmented Transition Nets (ATN), Semantic Analysis, Case and Logic Grammars, Planning Overview – An Example Domain: The Blocks Word, Component of Planning Systems, Goal Stack Planning (linear planning), Non-linear Planning using constraint posting, Probabilistic Reasoning and Uncertainty, Probability theory, Bayes Theorem and Bayesian networks, Certainty Factor.

12.00
Unit V: 
Expert Systems

Introduction to Expert Systems, Architecture of Expert Systems, Expert System Shells, Knowledge Acquisition, Case Studies, MYCIN, Learning, Rote Learning, Learning by Induction, explanation based learning.

ESSENTIAL READINGS: 
  • Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill, 3rd edition, 2009.
  • Dan W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice Hall of India, 1st edition, 1990.
REFERENCES: 
  • Nils J. Nilsson, “Principles of Artificial Intelligence (Symbolic Computation / Artificial Intelligence)”, reprint edition, 2014.
  • Stuart Russell, Peter Norving, “Artificial Intelligence: A Modern Approach”, Pearson Education, 3rd edition, 2010.
  • Winston, Patrick, Henry, “Artificial Intelligence”, Pearson Education, 3rd edition, 2004.
Academic Year: 
2018-19
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