MC9294 ARTIFICIAL INTELLIGENCE Syllabus for 5th Sem MCA - Fifth semester - Regulation 2009 - Anna University


MC9294                                 ARTIFICIAL INTELLIGENCE                                   LT P C
3 0 0 3
UNIT I             INTRODUCTION                                                                                       8
Intelligent Agents Agents and environments Good behavior – The nature of environments  structure of agents Problem Solving problem solving agents example problems searching for solutions uniformed search strategies avoiding repeated states searching with partial information.

UNIT II            SEARCHING TECHNIQUES                                                                  10
Informed search strategies heuristic function local search algorithms and optimistic problems local search in continuous spaces – online search agents and unknown environment  Constraint satisfaction problems (CSP) Backtracking search and Local  search     Structure  of  problems   AdversariaSearch   Games   Optimal decisions in games Alpha Beta Pruning imperfect real-time decision games that include an element of chance.

UNIT III           KNOWLEDGE REPRESENTATION                                                       10
First  order  logic  -  syntax  and  semantics  –  Using  first  order  logic   Knowledge engineering  Inference   prepositional versus first order logic unification and lifting forward chaining – backward chaining  Resolution – Knowledge representation – Ontological Engineering  Categories and objects Actions – Simulation and events – Mental events and mental objects.

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 – Inductive logic programming - Statistical learning methods –   Learning with complete data  Learning with hidden variable  EM algorithm –   Instance based learning  Neural networks Reinforcement  learning   Passive  reinforcement  learning  –      Active  reinforcement learning Generalization in reinforcement learning.

UNIT V           APPLICATIONS                                                                                        8
Communication Communication as action Formal grammar for a fragment of English
Syntactic analysis Augmented grammars – Semantic interpretation Ambiguity and disambiguation   Discourse  understanding   Grammar  induction  –     Probabilistic
language processing –   Probabilistic language models Information retrieval – Information Extraction Machine translation.
TOTAL : 45 PERIODS REFERENCES
1.  Stuart Russell, Peter Norvig, Artificial Intelligence A Modern Approach, Second
Edition, Pearson Education / Prentice Hall of  India, 2004.
2.  Nils J. Nilsson, Artificial Intelligence: A new Synthesis, Harcourt Asia Pvt. Ltd.,
2000.
3.  Elaine Rich and Kevin Knight, Artificial Intelligence, Second Edition, Tata McGraw
Hill, 2003.
4.  George  F.  Luger,  Artificial  Intelligence-Structures  And  Strategies  For  Complex
Problem Solving, Pearson Education / PHI, 2002.
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