# InfoCoBuild

## Artificial Intelligence: Knowledge Representation and Reasoning

Artificial Intelligence: Knowledge Representation and Reasoning. Instructor: Prof. Deepak Khemani, Department of Computer Science and Engineering, IIT Madras. 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. (from nptel.ac.in)

 Lecture 20 - Programming in a Rule Based Language

Go to the Course Home or watch other lectures:

 Introduction Lecture 01 - Introduction Lecture 02 - Introduction to Knowledge Representation and Reasoning Lecture 03 - An Introduction to Formal Logics Lecture 04 - Propositional Logic: Language, Semantics and Reasoning Lecture 05 - Propositional Logic: Syntax and Truth Values Propositional Logic Lecture 06 - Valid Arguments and Proof Systems Lecture 07 - Rules of Inference and Natural Deduction Lecture 08 - Axiomatic Systems and Hilbert Style Proofs Lecture 09 - The Tableau Method Lecture 10 - The Resolution Refutation Method First Order Logic Lecture 11 - Syntax Lecture 12 - Semantics Lecture 13 - Entailment and Models Lecture 14 - Proof Systems Lecture 15 - Forward Chaining Lecture 16 - Unification Rule Based Systems Lecture 17 - Forward Chaining Rule Based Systems Lecture 18 - The Rete Algorithm Lecture 19 - The Rete Algorithm: Example Lecture 20 - Programming in a Rule Based Language Lecture 21 - The OPS5 Expert System Shell Representation in First Order Logic Lecture 22 - Skolemization Lecture 23 - Terminological Facts Lecture 24 - Properties and Categories Lecture 25 - Reification and Abstract Entities Lecture 26 - Resource Description Framework (RDF) Lecture 27 - The Event Calculus: Reasoning about Change Natural Language Understanding Lecture 28 - Natural Language Semantics Lecture 29 - Conceptual Dependency (CD) Theory Lecture 30 - Conceptual Dependency (CD) Theory (cont.) Lecture 31 - English to CD Theory Logic Programming with Prolog Lecture 32 - Backward Chaining Lecture 33 - Logic Programming Lecture 34 - Prolog Lecture 35 - Search in Prolog Lecture 36 - Controlling Search Lecture 37 - The Cut Operator in Prolog Resolution Refutation in First Order Logic Lecture 38 - Incompleteness Lecture 39 - The Resolution Method for First Order Logic Lecture 40 - Clause Form Lecture 41 - First Order Logic with Equality Lecture 42 - Complexity of Resolution Refutation Knowledge Structures Lecture 43 - Semantic Nets and Frames Lecture 44 - Scripts Lecture 45 - Applying Scripts Lecture 46 - Goals, Plans and Actions Lecture 47 - Plan Applier Mechanism Lecture 48 - Top Down and Bottom Up Reasoning Description Logic Lecture 49 - Introduction Lecture 50 - Normalisation Lecture 51 - Structure Matching Lecture 52 - Structure Matching: Example Lecture 53 - Classification Lecture 54 - A-Box Reasoning Description Logic and Inheritance Lecture 55 - Description Logic: Extensions Lecture 56 - Description Logic: ALC Lecture 57 - ALC Examples Lecture 58 - Taxonomies and Inheritance Lecture 59 - Beliefs Lecture 60 - Inheritance Hierarchies Default Reasoning Lecture 61 - Introduction Lecture 62 - Circumscription Lecture 63 - Circumscription (cont.) Lecture 64 - Minimal Models Lecture 65 - Event Calculus Revisited Lecture 66 - Circumscription in Event Calculus Epistemic Logic Lecture 67 - Default Logic Lecture 68 - Autoepistemic Logic Lecture 69 - Epistemic Logic Lecture 70 - The Muddy Children Puzzle