Tuesday, July 10, 2012

IT9023-ARTIFICIAL INTELLIGENCE-ANNA UNIVERSITY SYLLABUS IT


IT9023-ARTIFICIAL INTELLIGENCE-ANNA UNIVERSITY SYLLABUS IT


IT9023                           ARTIFICIAL INTELLIGENCE                                L T P C
3  0 0 3
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 techniques; 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 searc  Structur 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 chaining Knowledge  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.
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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.





TEXT BOOKS:


TOTAL : 45 PERIODS



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 Artificia Intelligence Second   Edition,   Tata
McGraw Hill, 2003.
3.   George  F. Luger,  Artificial  Intelligence-Structures   And  Strategies  For  Complex
Problem Solving, Pearson Education, 2002.


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7/10/2012 12:24:00 PM

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