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Course Detail

Course Name Artificial Intelligence
Course Code 24CLT674
Program M. Sc. Cognitive Sciences, Learning and Technology
Semester Soft Core
Credits 3
Campus Amritapuri


Unit I

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.

Unit II

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).

Unit III

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).

Unit IV

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.



  • Machine Learning
  • Programming languages
  • Probability

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 and Outcomes

Course Objectives

  • Illustrate the reasoning on Uncertain Knowledge
  • Explore the explanation-based learning in solving AI problems
  • To explore advanced career opportunities.
  • Demonstrate the applications of soft computing and Evolutionary Computing algorithms

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


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 1




PO 3




PO 5










PO1 0


PO1 1


PO1 2

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

Textbooks / References

  1. Artificial Intelligence (Second Edition) – Elaine Rich, Kevin knight (Tata McGraw-Hill)
  2. A Guide to Expert Systems – Donald A. Waterman (Addison-Wesley)
  3. Principles of Artificial Intelligence – Nils J. Nilsson (Narosa Publishing House)
  4. Introduction to Artificial Intelligence – Eugene Charnaik, Drew McDermott (Pearson Education Asia).

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