Artificial Intelligence: Knowledge Representation And Reasoning | IIT Madras Free Courses

Artificial Intelligence: Knowledge Representation And Reasoning | IIT Madras Free Courses


An intelligent agent needs to be able to solve problems in its world. The ability to create representations of the domain of interest and reason with these representations is a key to intelligence. In this course we explore a variety of representation formalisms and the associated algorithms for reasoning. We start with a simple language of propositions, and move on to first order logic, and then to representations for reasoning about action, change, situations, and about other agents in incomplete information situations. This course is a companion to the course “Artificial Intelligence: Search Methods for Problem Solving” that was offered recently and the lectures for which are available online. 

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INTENDED AUDIENCE : BE/ME/MS/MSc/PhD students PREREQUISITES : Some exposure to formal languages, logic and programming INDUSTRY SUPPORT : Software companies dealing with knowledge and reasoning, including the semantic web and semantic search.Summary

Course Status :Upcoming
Course Type :Elective
Duration :12 weeks
Start Date :24 Jan 2022
End Date :15 Apr 2022
Exam Date :23 Apr 2022 IST
Category :Computer Science and EngineeringArtificial IntelligenceData Science
Credit Points :3
Level :Undergraduate/Postgraduate

This is an AICTE approved FDP course

Course layout

Week 1: Introduction, Propositional Logic, Syntax and Semantics 
Week 2: Proof Systems, Natural Deduction, Tableau Method, Resolution Method 
Week 3: First Order Logic (FOL), Syntax and Semantics, Unification, Forward Chaining 
Week 4: The Rete Algorithm, Rete example, Programming Rule Based Systems 
Week 5: Representation in FOL, Categories and Properties, Reification, Event Calculus
Week 6: Deductive Retrieval, Backward Chaining, Logic Programming with Prolog 
Week 7: Resolution Refutation in FOL, FOL with Equality, Complexity of Theorem Proving 
Week 8: Description Logic (DL), Structure Matching, Classification 
Week 9: Extensions of DL, The ALC Language, Inheritance in Taxonomies 
Week 10: Default Reasoning, Circumscription, The Event Calculus Revisited 
Week 11: Default Logic, Autoepistemic Logic, Epistemic Logic, Multi Agent Scenarios

Optional Topics A: Conceptual Dependency (CD) Theory, Understanding Natural Language 
Optional Topics B: Semantic Nets, Frames, Scripts, Goals and Plans 

Books and references

Books followed in the course:

  1. Ronald J. Brachman, Hector J. Levesque: Knowledge Representation and Reasoning, Morgan Kaufmann, 2004.
  2. Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India), 2013. 

Supplementary Reading:

  1. Schank, Roger C., Robert P. Abelson: Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum, 1977.
  2. R. C. Schank and C. K. Riesbeck: Inside Computer Understanding: Five Programs Plus Miniatures, Lawrence Erlbaum, 1981.
  3. Murray Shanahan: A Circumscriptive Calculus of Events. Artificial Intelligence 77(2), pp. 249-284, 1995.
  4. John F. Sowa: Conceptual Structures: Information Processing in Mind and Machine, Addison–Wesley Publishing Company, Reading Massachusetts, 1984.
  5. John F. Sowa: Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks/Cole, Thomson Learning, 2000. 

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