ARTIFICIAL INTELLIGENCE L T P C syllabus

CS2351 ARTIFICIAL INTELLIGENCE L T P C
3 0 0 3
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
TEXT BOOK:
TOTAL: 45 PERIODS
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.