ARTIFICIAL INTELLIGENCE  SYLLABUS r-2008 

 

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

Artificial Intelligence syllabus r-2008

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