CS61 ARTIFICIAL
INTELLIGENCE L
T P C 3 0 0 3
UNIT I PROBLEM
SOLVING
Introduction – Agents – Problem
formulation – uninformed search strategies – heuristics
– informed search strategies –
constraint satisfaction
UNIT II LOGICAL
REASONING
Logical agents – propositional
logic – inferences – first-order logic – inferences in firstorder
logic – forward chaining –
backward chaining – unification – resolution
UNIT III
PLANNING
Planning with state-space search
– partial-order planning – planning graphs – planning
and acting in the real world
UNIT IV
UNCERTAIN KNOWLEDGE AND REASONING
Uncertainty – review of
probability - probabilistic Reasoning – Bayesian networks –
inferences in Bayesian networks –
Temporal models – Hidden Markov models
UNIT V LEARNING
Learning from observation -
Inductive learning – Decision trees – Explanation based
learning – Statistical Learning
methods - Reinforcement Learning
TOTAL: 45PERIODS
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.
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