28-02-2013, 02:19 PM
CS 2351 ARTIFICIAL INTELLIGENCE 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
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