Unit 1
What is Artificial Intelligence? – The AI Problems – The Underlying Assumption – What is an AI technique – Criteria for Success. Problems, Problem Spaces and Search – Defining Problem as a State Space Search – Production Systems – Problem Characteristics – Production System Characteristics – Issues in the design of Search Programs.
Unit 2
Heuristic Search Techniques – Generate – and – Test – Hill Climbing – Best-First Search – Problem Reduction – Constraint Satisfaction – Means – Ends Analysis. Knowledge Representation issues – Representations and Mapping – Approaches to knowledge Representation – Issues in knowledge Representation – The Frame Problem. Using Predicate Logic – Representing simple facts in Logic – Representing Instance and Isa Relationship – Computable Functions and Predicates – Resolution – Natural Deduction.
Unit 3
Representing Knowledge Using Rules – Procedural versus Declarative knowledge – Logic Programming – Forward versus Backward Reasoning – Matching – Control Knowledge. Symbolic Reasoning under Uncertainty – Introduction to Non-monotonic Reasoning – Augmenting a Problem Solver – Implementation: Depth – First Search. Statistical Reasoning – Probability and Baye’s Theorem – Bayesian Networks – Fuzzy Logic.
Unit 4
Game Playing – The Minimax Search Procedure – Adding Alpha-Beta Cutoffs. Understanding – What is Understanding? What makes Understanding hard?
Unit 5
Common Sense – Qualitative Physics – Commonsense ontology – Memory Organization – Expert Systems – Representing and Using Domain knowledge – Expert System Shells – knowledge Acquisition – Components of an AI program.