BCA Semester IV - Artificial Intelligence

Unit I

Introduction & Problem Solving: AI problems, AI Technique, Defining problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics.

Heuristic Search Techniques: Generate – and – test, Hill Climbing, Best–First Search, Problem Reduction, Constraint Satisfaction, Means-ends Analysis.

Unit II

Game Playing: Overview, Min-Max search Procedure, Adding Alpha-beta Cutoffs, Additional Refinements, Iterative Deepening. Knowledge Representation Issues: Approaches, Issues, Frame Problem, Using Predicate Logic: Representing simple facts in logic, Representing Instance and ISA Relationships, Computable Functions and predicates, Resolution, Natural Deduction.

Unit III

Uncertainty and Reasoning Techniques: Non-monotonic reasoning, Logics for Non-monotonic reasoning, Implementation issues, Augmenting a problem solver, implementation of Depth First Search and Breadth-first search. Statistical reasoning: Probability and Bayes theorem, Certainty factors and Rule-based systems, Bayesian Networks, Dempster-Shafer Theory.

Unit IV

Learning: What is Learning, Rote learning, Learning by taking advice, Learning in problem-solving, learning from examples: Induction, Learning by Decision trees—expert System: Representing and Using Domain Knowledge, Expert systems shells, Explanation, and Knowledge Acquisition.

Unit V

Perception and Action: Real Time Search, Vision, Speech Recognition, ACTION: Navigation, Manipulation, Robot architectures. Natural Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Statistical NLP, Spell Checking.