Artificial Intelligence Syllabus
Periods/week : 3 Periods & 1 Tut /week. Ses. : 30 Exam : 70 Examination (Practical): 3hrs. Credits: 4
Introduction to Artificial Intelligence, Artificial Intelligence Technique, Representation of a problem as State space search, production systems, Problem characteristics, Production System characteristics
Heuristic Search Technologies
Generate & Test Hill Climbing, Best First search, Problem reduction, Constraint satisfaction, Means
Proof with Backward Chaining, Resolution, question answering.
Representing Knowledge Using Rules:
Procedural Vs Declarative knowledge, Logic Programming, Forward Vs Backward Reasoning, Matching, Control Knowledge
Symbolic Reasoning with uncertainty
Non-monotonic Reasoning, Dependency – Directed Backtracking TMS.
Statistical Reasoning with Bayes Theorem, certainty Factors & Rule Based System, DS- Theory.
Weak & Strong Slot Filler Structures
Semantic nets, Frames, Conceptual dependencies, Scripts
Block world, Components of a Planning System, Goal State Planning, Non Linear Planning, Hierarchical Planning.
Natural Language Processing
Syntactic Analysis, Semantic Analysis, Discuses and Pragmatic Processing.
Representing and Using Domain Knowledge, Expert Systems Shells, Explanation
1. Artificial Intelligence, Rich E & Knight K – Tata Mcgrahill (1991)
2. Introduction to Artificial Intelligence & Expert Systems, Paterson. PHI