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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 02 - Introduction to Knowledge Representation and Reasoning


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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