Course

Unit I

AI problems, the foundation of AI and history of AI intelligent agents: Agents and Environments, Strategies – Control Strategy – State, Space, Search, – Stages of AI – Tasks in AI – AI Problem formulation with assumptions.

Unit II

Searching for solutions – Tic-Toc-Toe – Uniformed search strategies – Breadth-first search – Depth-first Search – Search with partial information (Heuristic search) Hill climbing – A* – AO* – Means-End Analysis

Unit III

Knowledge representation issues – predicate logic – logic programming, semantic nets – frames and inheritance – Constraint propagation – Representing knowledge using rules – Rules-based deduction systems. Reasoning under uncertainty – Review of probability in AI – Baye’s probabilistic – Maximum Likelihood Estimation – Interferences and Dempstershafer theory.

Unit IV

First-order logic – Resolution method – Inference in first-order logic – Propositional knowledge vs. first-order inference – Unification & lifts – Forward chaining – Backward chaining – Resolution – Learning from observation Inductive learning Classification: Decision trees – Explanation-based learning – Statistical Learning methods – Reinforcement Learning, fundamentals of neural networks.

Course Objective(s) 

• Familiarize the basic concepts, principles, and techniques of artificial intelligence. 

• Identify the various features of AI, further studies in AI-related fields or for careers in industries where AI technologies are increasingly prevalent. 

Course Outcomes 

CO-PO Mapping 

Textbooks:

• Artificial Intelligence (Second Edition) – Elaine Rich, Kevin knight (Tata McGraw-Hill)

References:

• S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, SecondEdition, Pearson Education
• David Poole, Alan Mackworth, Randy Goebel, “Computational Intelligence: a logical approach”, Oxford University Press.
• G. Luger, “Artificial Intelligence: Structures and Strategies for complex problemsolving”, Fourth Edition, Pearson Education.