CS8691- ARTIFICIAL INTELLIGENCE Syllabus 2017 Regulation

0
1942

CS8691- ARTIFICIAL INTELLIGENCE Syllabus 2017 Regulation

CS8691- ARTIFICIAL INTELLIGENCE Syllabus 2017 Regulation

CS8691 Syllabus 2017 Regulation

 CS8691                                ARTIFICIAL INTELLIGENCE                                L T P C
                                                                                                                           3 0 0 3

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

                                                                                                      TOTAL :45 PERIODS

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.

TEXT BOOKS:

  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.

REFERENCES:

  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.

LEAVE A REPLY

Please enter your comment!
Please enter your name here