Главная
Study mode:
on
1
Introduction
2
Introduction to Knowledge Representation and Reasoning
3
An Introduction to Formal Logics
4
Propositional Logic: Language, Semantics and Reasoning
5
Propositional Logic: Syntax and Truth Values
6
Propositional Logic: Valid Arguments and Proof Systems
7
Propositional Logic: Rules of Inference and Natural Deduction
8
Propositional Logic: Axiomatic Systems and Hilbert Style Proofs
9
Propositional Logic: The Tableau Method
10
Propositional Logic: The Resolution Refutation Method
11
Syntax
12
Semantics
13
Entailment and Models
14
Forward Chaining
15
Unification
16
Proof Systems
17
Forward Chaining Rule Based Systems
18
The Rete Algorithm
19
Rete Algorithm - Example
20
The OPS5 Expert System Shell
21
Programming in a Rule Based Language
22
Skolemization
23
Terminological Facts
24
Properties and Categories
25
Reification and Abstract Entities
26
The Event Calculus: Reasoning About Change
27
Resource Description Framework (RDF)
28
Natural Language Semantics
29
CD Theory
30
CD Theory (contd)
31
English to CD Theory
32
Natural Language Semantics
33
Backward Chaining
34
Logic Programming
35
Prolog
36
Search in Prolog
37
Controlling Search
38
The Cut Operator in Prolog
39
Incompleteness
40
M7 Lec 2 - The Resolution Refutation method for First Order Logic
41
Clause Form
42
FOL with Equality
43
Complexity of Resolution Refutation
44
The Resolution Method for FOL
45
Semantic Nets and Frames
46
Scripts
47
Applying Scripts
48
Goals, Plans and Actions
49
Plan Applier Mechanism
50
Top Down and Bottom Up Reasoning
51
Introduction
52
Normalisation
53
Structure Matching
54
Structure Matching - Example
55
Classification
56
A-box reasoning
57
DL: Extensions
58
DL: ALC
59
ALC examples
60
Taxonomies and Inheritance
61
Beliefs
62
Inheritance Hierarchies:
63
Event Calculus Revisited
64
Minimal Models
65
Circumscription (contd)
66
Circumscription
67
Introduction.
68
Circumscription in EC
69
Autoepistemc Logic
70
Defaul Logic
71
The Muddy Children Puzzle
72
Epistemic Logic
Description:
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
Add to list
#Computer Science #Artificial Intelligence #Humanities #Philosophy #Logic #Propositional Logic #Personal Development #Self Improvement #Critical Thinking #Reasoning
00:00
-33:47
1 of 72

1 Introduction