COURSE OUTLINE: 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.
Artificial Intelligence: Knowledge Representation and Reasoning
NPTEL and Indian Institute of Technology Madras via YouTube
Overview
Syllabus
Introduction.
Introduction to Knowledge Representation and Reasoning.
An Introduction to Formal Logics.
Propositional Logic: Language, Semantics and Reasoning.
Propositional Logic: Syntax and Truth Values.
Propositional Logic: Valid Arguments and Proof Systems.
Propositional Logic: Rules of Inference and Natural Deduction.
Propositional Logic: Axiomatic Systems and Hilbert Style Proofs.
Propositional Logic: The Tableau Method.
Propositional Logic: The Resolution Refutation Method.
Syntax.
Semantics.
Entailment and Models.
Forward Chaining.
Unification.
Proof Systems.
Forward Chaining Rule Based Systems.
The Rete Algorithm.
Rete Algorithm - Example.
The OPS5 Expert System Shell.
Programming in a Rule Based Language.
Skolemization.
Terminological Facts.
Properties and Categories.
Reification and Abstract Entities.
The Event Calculus: Reasoning About Change.
Resource Description Framework (RDF).
Natural Language Semantics.
CD Theory.
CD Theory (contd).
English to CD Theory.
Natural Language Semantics.
Backward Chaining.
Logic Programming.
Prolog.
Search in Prolog.
Controlling Search.
The Cut Operator in Prolog.
Incompleteness.
M7 Lec 2 - The Resolution Refutation method for First Order Logic.
Clause Form.
FOL with Equality.
Complexity of Resolution Refutation.
The Resolution Method for FOL.
Semantic Nets and Frames.
Scripts.
Applying Scripts.
Goals, Plans and Actions.
Plan Applier Mechanism.
Top Down and Bottom Up Reasoning.
Introduction.
Normalisation.
Structure Matching.
Structure Matching - Example.
Classification.
A-box reasoning.
DL: Extensions.
DL: ALC.
ALC examples.
Taxonomies and Inheritance.
Beliefs.
Inheritance Hierarchies:.
Event Calculus Revisited.
Minimal Models.
Circumscription (contd).
Circumscription.
Introduction..
Circumscription in EC.
Autoepistemc Logic.
Defaul Logic.
The Muddy Children Puzzle.
Epistemic Logic.
Taught by
Artificial Intelligence
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Reviews
4.7 rating, based on 3 Class Central reviews
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Perfect course for begginers. It is very helpful for person seeking skills and sone kind of job.
The whole theory has been explained very simply and one can also take down very good notes throughout the class -
Deepak Sisodiya1503Actually This course was amazing and this was full AI based subject.this content was really really good. -
It's useful for my future invention and It may use to many others ...then ease improve more creative...
