CS8691 Artificial Intelligence Anna University Syllabus Regulation 17

CS8691 Artificial Intelligence Anna University Syllabus Regulation 17


OBJECTIVES:  To understand the various characteristics of Intelligent agents   To learn the different search strategies in AI  To learn to represent knowledge in solving AI problems  To understand the different ways of designing software agents  To know about the various applications of AI.

UNIT I     INTRODUCTION                                                                                                           9 Introduction–Definition - Future of Artificial Intelligence – Characteristics of Intelligent Agents– Typical Intelligent Agents – Problem Solving Approach to Typical AI problems.

UNIT II   PROBLEM SOLVING METHODS                                                                               9 Problem solving Methods - Search Strategies- Uninformed - Informed - Heuristics - Local Search Algorithms  and  Optimization  Problems  -  Searching  with  Partial  Observations  - Constraint Satisfaction Problems – Constraint Propagation - Backtracking Search - Game Playing - Optimal Decisions in Games – Alpha - Beta Pruning - Stochastic Games

UNIT III   KNOWLEDGE REPRESENTATION                                                                          9 First Order Predicate Logic – Prolog Programming – Unification – Forward Chaining-Backward Chaining – Resolution – Knowledge Representation - Ontological Engineering-Categories and Objects – Events - Mental Events and Mental Objects - Reasoning Systems for Categories - Reasoning with Default Information

UNIT IV         SOFTWARE AGENTS                                                                                             9 Architecture for Intelligent Agents – Agent communication – Negotiation and Bargaining – Argumentation among Agents – Trust and Reputation in Multi-agent systems.

UNIT V           APPLICATIONS                                                                                                       9 AI applications – Language Models – Information Retrieval- Information Extraction – Natural Language Processing - Machine Translation – Speech Recognition – Robot – Hardware – Perception – Planning – Moving                                                                                                               

OUTCOMES: Upon completion of the course, the students will be able to:   Use appropriate search algorithms for any AI problem  Represent a problem using first order and predicate logic  Provide the apt agent strategy to solve a given problem  Design software agents to solve a problem  Design applications for NLP that use Artificial Intelligence.

1 S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approach‖, Prentice Hall, Third Edition, 2009. 2 I. Bratko, ―Prolog: Programming for Artificial Intelligence‖, Fourth edition, Addison-Wesley Educational Publishers Inc., 2011.

1. M. Tim Jones, ―Artificial Intelligence: A Systems Approach(Computer Science)‖, Jones and Bartlett Publishers, Inc.; First Edition, 2008
2. Nils J. Nilsson, ―The Quest for Artificial Intelligence‖, Cambridge University Press, 2009.
3. William F. Clocksin and Christopher S. Mellish,‖ Programming in Prolog: Using the ISO Standard‖, Fifth Edition, Springer, 2003.
4. Gerhard Weiss, ―Multi Agent Systems‖, Second Edition, MIT Press, 2013.
5. David L. Poole and Alan K. Mackworth, ―Artificial Intelligence: Foundations of Computational Agents‖, Cambridge University Press, 2010.

Anna University Syllabus Regulation 17 (CSE Sem-6) Internet Programming

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