CS1351 – ARTIFICIAL INTELLIGENCE
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UNIT I FUNDAMENTALS 8
Intelligent agents * Agents and environments * Good behavior * The nature of
environments * Structure of agents * Problem solving * Problem solving agents *
Example problems * Searching for solutions * Uniformed search strategies *
Avoiding repeated states * Searching with partial information.
UNIT II SEARCHING TECHNIQUES 10
Informed search and exploration * Informed search strategies * Heuristic function *
Local search algorithms and optimistic problems * Local search in continuous spaces
* Online search agents and unknown environments * Constraint Satisfaction
Problems(CSP) * Backtracking Search and Local Search for CSP * Structure of
problems * Adversarial search * Games * Optimal decisions in games * Alpha-Beta
pruning * Imperfect real-time decision * Games that include an element of chance.
UNIT III KNOWLEDGE REPRESENTATION 10
First order logic * Representation revisited * Syntax and semantics for first order
logic * Using first order logic * Knowledge engineering in first order logic *
Inference in first order logic * Propositional versus first order logic * Unification and
lifting * Forward chaining * Backward chaining * Resolution * Knowledge
representation * Ontological engineering * Categories and objects * Actions *
Simulation and events * Mental events and mental objects.
UNIT IV LEARNING 9
Learning from observations * Forms of learning * Inductive learning * Learning
decision trees * Ensemble learning * Knowledge in learning * Logical formulation of learning * Explanation based learning * Learning using relevant information *
Inductive logic programming * Statistical Learning Methods * Learning with
Complete Data * Learning with Hidden Variable * EM Algorithm * Instance Based
Learning * Neural Networks * Reinforcement Learning * Passive Reinforcement
Learning * Active reinforcement learning * Generalization in reinforcement learning.
UNIT V APPLICATIONS 8
Communication * Communication as action * Formal grammar for a fragment of
english * Syntactic analysis * Augmented grammars * Semantic interpretation *
Ambiguity and disambiguation * Discourse understanding * Grammar Induction *
Probabilistic language processing * Probabilistic language models * Information
Retrieval * Information extraction * Machine translation.
Engineering students people interested in learning Robotics