CS464 ARTIFICIAL INTELLIGENCE

Course Objectives:
To introduce basic principles that drive complex real world intelligence applications.
To introduce and discuss the basic concepts of AI Techniques and Learning

Syllabus:
Introduction to AI, Solving Problems by Searching-uninformed, informed, heuristic,
constraint Satisfaction problems -AI Representational Schemes-Learning-Advanced
searches-Alpha beta pruning, Expert Systems-Natural Language Processing Concepts.

Module 1

Introduction: What is AI, The foundations of AI, History and applications, Production systems. Structures and strategies for state space search. Informed and Uninformed searches.

Module 2

Search Methods: data driven and goal driven search. Depth first and breadth first search, DFS with iterative deepening. Heuristic search-best first search, A * algorithm.AO* algorithm, Constraint Satisfaction. Crypt Arithmetic Problems

Module 3

AI representational schemes- Semantic nets, conceptual dependency, scripts, frames, introduction to agent based problem solving, Machine learning-symbol based-a frame work for symbol based learning.

Module 4

Advanced Search: Heuristics in Games, Design of good heuristic-an example. Min-Max Search Procedure, Alpha Beta pruning

Module 5

Learning Concepts: Version space search. Back propagation learning. Social and emergent models of learning-genetic algorithm, classifier systems and genetic programming.

Module 6

Expert Systems: rule based expert systems. Natural language processing-natural language understanding problem, deconstructing language. Syntax stochastic tools for language
analysis, natural language applications

Text Books:

1. E Rich, K Knight, Artificial Intelligence, 3/e, Tata McGraw Hil, 2009.
2. George.F.Luger, Artificial Intelligence- Structures and Strategies for Complex
   Problem Solving, 4/e, Pearson Education. 2002.

Question Bank