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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.  

ModuleNo.

Topics

No.of Hours

1.

Introduction

1.5

2.

Propositional Logic
Language, Semantics and Reasoning, Syntax and Truth Values, Valid Arguments and Proof Systems, Rules of Inference and Natural Deduction, Axiomatic Systems and Hilbert Style Proofs, The Tableau Method, The Resolution Refutation Method

3.5

3.

First Order Logic (FOL)
Syntax, Semantics, Entailment and Models, Proof Systems, Forward Chaining, Unification, Forward Chaining Rule Based Systems, The Rete Algorithm, Programming in a  Rule Based Language, The OPS5 Expert System Shell

5.5

4.

Representation in FOL
Skolemization, Knowledge Representation, Properties and Categories, Reification and Abstract Entities, Resource Description Framework (RDF), The Event Calculus: Reasoning About Change

3

5.

Mapping Natural Language to FOL
Understanding = Fulfilling Expectations, Conceptual Dependency (CD) Theory,    Understanding Language, Conceptual Analysis: Mapping English to CD Theory

2

6.

Programming in Logic
Deductive Retrieval in Backward Chaining, Logic Programming, Prolog, Depth First Search and Efficiency Issues, Controlling Search, The Cut Operator in Prolog

3

7.

Theorem Proving in FOL
Incompleteness of Forward and Backward Chaining,  The Resolution Refutation Method for FOL, Clause Form and The Resolution Rule, FOL with Equality, Complexity

2.5

8.

Knowledge Structures
Semantic Nets using Frames, Scripts, Script Applier Mechanism (SAM), Goals, Plans and Actions, Plan Applier Mechanism (PAM): Expectations and Recognition, PAM: Top Down and Bottom Up Reasoning

3

9.

Ontology and Description Logics
A Description Logic, Normalisation, Structure Matching, Classification, A-box Reasoning, Extensions, ALC, Further Extensions

4.5

10.

Inheritance
Taxonomies and Inheritance, Beliefs,  Credulous and Skeptical Reasoning

1.5

11.

Default Reasoning
Introduction to Default Reasoning, Circumscription, Minimal Models, The Event Calculus Revisited, Default Logic, Autoepistemc Logic

4

12.

Reasoning in Multi-agent Systems
Epistemic Logic: Kripke Semantics in a Multi Agent Scenario, The Muddy Children Puzzle

1

Some exposure to formal languages, logic and programming


Text Books

  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.

Reference Books/Articles

  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. Artif. Intell. 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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