MC9294 ARTIFICIAL INTELLIGENCE SYLLABUS | ANNA UNIVERSITY MCA 5TH SEM SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY FIFTH SEMESTER M.C.A. (MASTER OF COMPUTER APPLICATIONS) DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,PREVIOUS YEAR QUESTION PAPERS,MODEL QUESTION PAPERS, CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2009 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
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
environments – Constraint satisfaction problems (CSP) – Backtracking search and
Local search – Structure of problems – Adversarial Search – Games – Optimal
decisions in games – Alpha – Beta Pruning – imperfect real-time decision – games that
include an element of chance.
42
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.
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
environments – Constraint satisfaction problems (CSP) – Backtracking search and
Local search – Structure of problems – Adversarial Search – Games – Optimal
decisions in games – Alpha – Beta Pruning – imperfect real-time decision – games that
include an element of chance.
42
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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