| Course Name | Artificial Intelligence |
| Course Code | 24CLT674 |
| Program | M. Sc. Cognitive Sciences, Learning and Technology |
| Semester | Soft Core |
| Credits | 3 |
| Campus | Amritapuri |
Artificial Intelligence – Basics, 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. Case study based on search algorithms (to be considered as part of continuous assessment).
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.
Case study based on reasoning (to be considered as part of continuous assessment).
Reasoning under Uncertainty – Introduction to Non-monotonic Reasoning – Augmenting a Problem Solver –
Implementation: Depth – First Search, Fuzzy Logic.
Game Playing – The Minimax Search Procedure – Adding Alpha-Beta Cut-offs. Applications of artificial intelligence- Case study on social networks using neural networks, DNA sequencing using AI techniques.
Prerequisites
Summary:
Gain a historical perspective of AI and its foundations. Become familiar with basic principles of AI toward problem solving and intuitive understanding of approaches of inference, perception, knowledge representation, and learning.
Course Objectives
Course Outcomes
| Cos | Description |
| CO1 | To be aware of the basics of AI and its need along with the issues in designing search problems. |
| CO2 | Understand and apply various search algorithms in real world problems. |
| CO3 | To get a thorough idea about the fundamentals of knowledge representation, inference and theorem
proving. |
| CO4 | Express and comprehend the working knowledge of reasoning in the presence of incomplete and/or
uncertain information. |
| CO5 | To gain the aptitude to apply knowledge representation and reasoning to real-world problems. |
CO-PO Mapping
| PO/PS O |
PO 1 |
PO2 |
PO 3 |
PO4 |
PO 5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO1 0 |
PO1 1 |
PO1 2 |
| CO | ||||||||||||
| CO1 | 2 | 2 | 2 | 1 | – | – | – | – | 2 | 1 | 1 | 2 |
| CO2 | 2 | 1 | 1 | – | – | 1 | – | 2 | 2 | 1 | 1 | 2 |
| CO3 | 3 | 2 | 1 | 1 | – | – | – | – | 2 | 1 | – | 2 |
| CO4 | 1 | 2 | 1 | 1 | – | 1 | 1 | 2 | 1 | 1 | 1 | 2 |
| CO5 | 2 | 1 | 1 | 1 | – | – | 1 | 2 | 1 | 1 | 1 | 2 |
Evaluation Pattern:
| Assessment | Inter nal | External |
| Active Participation in Class | 10 | |
| *Continuous Assessment (CA) | 40 | |
| Content produced over the course and submitted at the last | 50 |
*CA – Can be Quizzes, Assignment, Projects, and Reports, and Seminar
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