AI History and Applications: From Eden to ENIAC-Historical foundation-Development of Logic- Turing Test. AI Challenge: Knowledge representation: Issues in representation-Conceptual graphs-Alternative representations and ontologies.

Machine Learning-Symbol based: Introduction-Framework for Symbol based learning-Knowledge and learning-Reinforcement Learning-Machine Learning-Connectionist: Foundation for Connectionist Networks- Perception Learning-Competitive Learning.

Planning: The Planning Problem-Planning with state space search-Partial order planning-Planning Graphs-Analysis of Planning approaches. Planning and Acting in real world: Time, Schedules and Resources- Conditional Planning-Execution, Monitoring and replanning –Continuous planning.

Uncertainty: Acting under uncertainty- Basic probability Notion-Bayes’s Rule and its use. Reinforcement Learning: Introduction-Passive Reinforcement Learning-Active Reinforcement Learning- Generalization Reinforcement Learning-Policy Search

Overview of AI application areas. AI as Representation and search: The predicate calculus: The propositional calculus-Predicate Calculus-Application. AI as Empirical Enquiry: Introduction-The Science of Intelligent System-AI current challenges & future directions.