| Course Name | Introduction to AI |
| Course Code | 25CSA301 |
| Program | B. Sc. in Physics, Mathematics & Computer Science (with Minor in Artificial Intelligence and Data Science) |
| Semester | 5 |
| Credits | 3 |
| Campus | Mysuru |
What is Artificial Intelligence? – The AI Problems – The Underlying Assumption – What is an AI technique – Criteria for Success.
Problems, Problem Spaces and Search – Defining Problem as a State Space Search – Production Systems – Problem Characteristics – Production System Characteristics – Issues in the design of Search Programs.
Heuristic Search Techniques – Generate – and – Test – Hill Climbing – Best-First Search – Problem Reduction – Constraint Satisfaction – Means – Ends Analysis. Knowledge Representation issues – Representations and Mapping
Approaches to knowledge Representation – Issues in knowledge Representation – The Frame Problem. Using Predicate Logic – Representing simple facts in Logic – Representing Instance and Isa Relationship – Computable Functions and Predicates – Resolution – Natural Deduction.
Representing Knowledge Using Rules – Procedural versus Declarative knowledge – Logic Programming – Forward versus Backward Reasoning – Matching – Control Knowledge.
Symbolic Reasoning under Uncertainty – Introduction to Non-monotonic Reasoning – Augmenting a Problem Solver – Implementation: Depth – First Search.
Statistical Reasoning – Probability and Baye’s Theorem – Bayesian Networks – Fuzzy Logic.
Game Playing – The Minimax Search Procedure – Adding Alpha-Beta Cutoffs. Understanding – What is Understanding? What makes Understanding hard?
Common Sense – Qualitative Physics – Commonsense ontology – Memory Organization – Expert Systems – Representing and Using Domain knowledge – Expert System Shells – knowledge Acquisition – Components of an AI program.
Course Outcome
| Cos | Description |
| CO1 | Demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations. |
| CO2 | Explain basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning |
| CO3 | Explore the current scope, potential, limitations, and implications of intelligent systems. |
| CO4 | Explain the various searching techniques, constraint satisfaction problem and example problems- game playing techniques. |
| CO5 | Experiment an AI model for simulation and analysis. |
TEXTBOOKS:
1) Artificial Intelligence (Second Edition) – Elaine Rich, Kevin knight (Tata McGraw-Hill)
2) A Guide to Expert Systems – Donald A. Waterman (Addison-Wesley)
REFERENCES:
1) Principles of Artificial Intelligence – Nils J. Nilsson (Narosa Publishing House)
2) Introduction to Artificial Intelligence – Eugene Charnaik, Drew McDermott (Pearson Education Asia)
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