MCA-305: Artificial Intelligence

Lectures: 4 Periods/Week Sessional Marks: 30
University Exam: 3 Hours University Examination Marks: 70


UNIT-I
What is AI?
The AI Problems, The Underlying Assumption, What is AI Technique?, The level of the Model, Criteria for Success.
Problems, Problem spaces & Search
Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues in the design of Search Programs, Additional Problems.
Heuristic search techniques
Generate and Test, Hill Climbing, Best First Search, Problem Reduction, Constraint Satisfaction, Means Ends Analysis.

UNIT-II
Knowledge Representation Issues
Representations and Mappings, Approaches to Knowledge Representation, Issues in Knowledge Representation, The Frame Problem
Using Predicate Logic
Representing Simple Facts in Logic, Representing Instance and Isa Relationships, Computable Functions and Predicates, Resolution, Natural Deduction
Representing knowledge using Rules
Procedural versus Declarative Knowledge, Logic Programming, Forward versus Backward Reasoning, Matching, Control Knowledge

UNIT-III
Symbolic Reasoning under Uncertainity
Introduction to Nonmonotonic Reasoning, Logics for Nonmonotonic Reasoning, Implementation Issues, Augmenting a Problem Solver, Implementation: Depth-First Search, Implementation: Breadth-First Search
Weak slot & filler Structures
Semantic Nets, Frames
Planning
Overview, An Example Domain : The Blocks World, Components of a Planning System, Goal Stack Planning, Nonlinear Planning Using Constraint Posting, Hierarchical Planning, Reactive Systems, Other Planning Techniques

UNIT-IV
Natural Language Processing
Introduction, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic Processing
Commonsense
Qualitative Physics, Commonsense Ontologies, Memory Organisation, Case-Based Reasoning
Expert Systems
Representing and Using Domain Knowledge, Expert System Shells, Explanation, Knowledge Acquisition

Text Books

  1. Knight K, “Artificial Intelligence”, TMH (1991)
    Chapters : 1 through 7, 9, 13, 15, 10 and 20
Reference Books
  1. Michael Negnevitsky, “Artificial Intelligence – A Guide to Intelligent Systems”, Second Edition, Pearson Education (2008)
  2. Winston P.H, “Artificial Intelligence”, Addision Wesley (1993)