Module 1: INTRODUCTION

Concept of AI, history, Overview of AI Application Areas, Problems, Problem Spaces and Search – Defining the problem as a State Space Search - Production systems, Intelligent Agents – Structure of Agents

Module 2: SEARCHING TECHNIQUES

Introduction to uninformed and informed search, Uninformed Search – Breadth First Search - Depth First Search – Depth-limited Search - Iterative Deepening Depth First Search – Bidirectional Search, Heuristic Search Techniques – Generate and Test – Hill Climbing – Best-First Search – A* Algorithm - Problem Reduction – AO* Algorithm - Game Search - Heuristics in Games-Min-Max Search Procedure, Alpha-Beta pruning

Module 3: KNOWLEDGE REPRESENTATION

Knowledge-Based Agents, First-Order Logic (Predicate Calculus) - syntax, Inference in First-Order Logic, Forward and Backward chaining – Resolution.

 Module 4: PLANNING

Planning - Definition of Classical Planning, Algorithms for Planning with State Space Search, Planning Graphs, other Classical Planning Approaches, Analysis of Planning approaches. Hierarchical Planning.  

Module 5: LEARNING CONCEPTS & EXPERT SYSTEMS

Learning – Rote Learning - Learning by taking Advice – Learning by Problem Solving –Learning from Examples – Decision Trees, Ensemble Learning, Explanation-Based Learning, Reinforcement Learning – Passive Reinforcement Learning – Active Reinforcement Learning. Introduction to Natural Language Processing.