Sunday, July 8, 2012

CS9304-ARTIFICIAL INTELLIGENCE-B.E -CSE-COMPUTER SCIENCE AND ENGINEERING FIFTH-V SEMESTER 2008 REGULATION ANNA UNIVERSITY SYLLABUS



CSE Computer Science And Engineering V-fifth Semester Syllabus 2008 Regulation Anna University


AIM:
The aim of this course is to provide an introduction to some basic issues and algorithms in artificial intelligence (AI). The course also provides an overview of Intelligent agent design, where agents perceive their environment and act rationally to fulfill their goals. The course approaches AI from an algorithmic, computer science-centric perspective.

OBJECTIVES:

·      To be familiar with the history of AI, philosophical debates, and be able to discuss the potential and limitations of the subject in its current form.
·      To identify the kind of problems that can be solved using AI technique: to know the relation between AI and other areas of computer science.
·     To have knowledge of generic problem-solving methods in AI.
·     To understand the basic techniques of knowledge representation and their use.
·     To know what the basic components of an intelligent agent are, and how this relates
to  other  advanced  subjects  such  as  information  retrieval,  database  systems, computer vision, robotics, human-computer interaction, reactive systems etc.
·      To be able to implement basic decision making algorithms, including search-based problem solving techniques, and first-order logic.

·      To know the basic issues in machine learning, and be able to apply straightforward techniques to learn from observed data.
·     To be able to explain the difficulty of computer perception with examples from
different modalities, and be able to show how perception affects intelligent systems design.

UNIT I           INTRODUCTION                                                                                         9
Intelligent  Agents  Environments   Behavior  –    Structure   Artificial  Intelligence  Present  and  Future  -  Problem  Solving  agents   examples–  uninformed  search strategies Avoiding repeated states searching with partial information.

UNIT II         SEARCHING TECHNIQUES                                                                        9
Informed search strategies greedy – best first A* - local search algorithms and optimization local search in continuous spaces Constraint satisfaction problems (CSP) Backtracking search and Local search   Structure Adversarial Search Games Optimal decisions in games – Alpha Beta Pruning imperfect real-time decision games elements of  chance.

UNIT III         KNOWLEDGE REPRESENTATION AND REASONING                           9
Logical Agents Wumpus world - Propositional logic - First order logic - syntax and semantics Using first order logic  Inference forward chaining backward chainingKnowledge  representation   Ontological  Engineering  –    Categories  and  objects  Actions Simulation and events Mental events and mental objects.
Reasoning with Default Information Truth Maintenance Systems – Reasoning with Uncertain Information Axioms of Probability Independence Bayes’ Rule and its use.

UNIT IV        LEARNING                                                                                                  9

Learning from observations –   forms of learning Inductive learning - Learning decision trees –   Ensemble learning –   Knowledge in learning Logical formulation of learning Explanation based learning Learning using relevant information - Reinforcement learning Passive reinforcement learning –      Active reinforcement learning Generalization in reinforcement learning.

UNIT V         APPLICATIONS                                                                                          9

Communication Communication as action Formal grammar for a fragment of English
Syntactic analysis Augmented grammars Semantic interpretation - Perception
image  Formation   Image  Processing   Object  Recognition   Robotics   Robotic
Perception Planning Moving Robotic Software Architecture.

TOTAL: 45 PERIODS



TEXT BOOK:
1.  Stuart Russell, Peter Norvig, Artificial Intelligence A Modern Approach, Second
Edition, Pearson Education, 2004.

REFERENCES:
1.  Nils J. Nilsson, Artificial Intelligence: A new Synthesis, Harcourt Asia Pvt. Ltd.,
2000.
2.  Elaine Rich and Kevin Knight, Artificial Intelligence, Second Edition, Tata McGraw
Hill, 2003.
3.  George  F.  Luger,  Artificial  Intelligence-Structures  And  Strategies  For  Complex
Problem Solving, Pearson Education, 2002.

7/08/2012 01:18:00 AM

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