CS2351 Artificial Intelligence-Syllabus

CS2351 ARTIFICIAL INTELLIGENCE

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AIM:

To learn the basics of designing intelligent agents that can solve general purpose problems, represent and process knowledge, plan and act, reason under uncertainty and can learn from experiences

UNIT I PROBLEM SOLVING 9

Introduction – Agents – Problem formulation – uninformed search strategies – heuristics

– informed search strategies – constraint satisfaction

UNIT II LOGICAL REASONING 9

Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder

logic – forward chaining – backward chaining – unification – resolution

UNIT III PLANNING 9

Planning with state-space search – partial-order planning – planning graphs – planning

and acting in the real world

UNIT IV UNCERTAIN KNOWLEDGE AND REASONING 9

Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks –

inferences in Bayesian networks – Temporal models – Hidden Markov models

UNIT V LEARNING 9

Learning from observation - Inductive learning – Decision trees – Explanation based

learning – Statistical Learning methods - Reinforcement Learning

TOTAL: 45

 PERIODS TEXT BOOK:

1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second

Edition, Pearson Education, 2003.

REFERENCES:

1. David Poole, Alan Mackworth, Randy Goebel, ”Computational Intelligence : a logical

approach”, Oxford University Press, 2004.

2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem

solving”, Fourth Edition, Pearson Education, 2002.

3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998.