Problem-Solving Methods : Understanding, Description, Development and Resue : Understanding, Description, Development and Resue - Dieter Fensel

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Problem-Solving Methods : Understanding, Description, Development and Resue

Understanding, Description, Development and Resue

By: Dieter Fensel

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Paperback | 1 October 2000

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172 Pages

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Researchers in Artificial Intelligence have traditionally been classified into two categories: the "neaties" and the "scruffies". According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it "an art", thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).

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Introduction	p. 1
What Are Problem-Solving Methods
Making Assumptions for Efficiency Reasons	p. 7
A Definition of a Task	p. 10
A Non-efficient Problem Solver	p. 11
An Efficient Problem Solver	p. 13
Summary of the Case Study	p. 17
The Twofold Role of Assumptions	p. 19
How Deal Other Approaches with Assumptions and Efficiency	p. 22
An Empirical Survey of Assumptions	p. 26
Assumptions Necessary to Define the Task	p. 27
Identifying Abnormalities	p. 27
Identifying Causes	p. 27
Defining Hypotheses	p. 29
Defining Diagnoses	p. 29
Summary	p. 30
Assumptions Necessary to Define an Efficient Problem Solver	p. 33
Reducing the Worst-Case Complexity	p. 34
Reducing the Average-Case Behavior	p. 35
Search Guidance	p. 36
Summary	p. 37
Assumptions in System-Environment Interaction	p. 38
Summary	p. 39
How to Describe Problem-Solving Methods
A Four Component Architecture for Knowledge-Based Systems	p. 43
The Entire Framework	p. 44
The Main Elements of a Specification	p. 44
The Main Proof Obligations	p. 48
Task	p. 49
Problem-Solving Method	p. 50
The Black Box Description: Competence and Requirements	p. 51
The Operational Specification	p. 51
Domain Model	p. 54
Adapters	p. 56
Connecting Task and Problem-Solving Method	p. 57
Connecting with the Domain Model	p. 58
Related Work	p. 58
Logics for Knowledge-Based Systems: MLPM and MCL	p. 61
Specification Languages for Knowledge-Based Systems	p. 62
(ML)²	p. 62
KARL	p. 66
Design Rationales for a Logic of Dynamics	p. 67
Logics for the Dynamics of Knowledge-Based Systems	p. 71
Modal Logic of Predicate Modification (MLPM)	p. 71
Modal Change Logic (MCL)	p. 73
Modeling MLPM with MCL	p. 73
Formalizing Other Approaches	p. 74
Formalizing KADS Languages	p. 74
Using MCL to Formalize Abstract State Machines	p. 75
Approaches Using Different Paradigms	p. 76
A Verification Framework for Knowledge-Based Systems	p. 78
The Architecture in KIV	p. 79
Formalizing a Task	p. 79
Formalizing a Problem-Solving Method	p. 81
Proving Total Correctness of the Problem-Solving Method	p. 84
Adapter: Connecting Task and Problem-Solving Method	p. 87
A Specific Pattern in Specifying Architectures of Knowledge-Based Systems	p. 88
Future Work	p. 90
How to Develop and Reuse Problem-Solving Methods
Methods for Context Explication and Adaptation	p. 95
Inverse Verification of Problem-Solving Methods	p. 98
First Example: A Local Search Method	p. 100
Second Example: Finding an Abductive Explanation	p. 103
Heuristic Assumptions	p. 105
Related Work	p. 108
Stepwise Adaptation of Problem-Solving Methods	p. 109
Local Search	p. 110
Hill-Climbing	p. 110
Set-Minimizer	p. 111
Abductive Diagnosis	p. 113
Generalization and Limitation of Refinement with Adapters	p. 114
Organizing a Library of Problem-Solving Methods	p. 116
The Three Dimensions in Method Organization	p. 117
Deriving Task-Specific Problem-Solving Methods	p. 119
Problem Type Design	p. 121
Local Search	p. 121
Local Search as Design Problem Solving	p. 122
Problem Type Parametric Design	p. 122
Local Search as Parametric Design Problem Solving	p. 123
Variating the Problem-Solving Paradigm	p. 123
Derive Successor Candidates	p. 124
Select the Design Model that Is to Be Expanded Next	p. 126
Update the Set of Future Candidates	p. 126
Conclusions	p. 127
Conclusions and Future Work	p. 129
References	p. 133
Table of Contents provided by Publisher. All Rights Reserved.

Problem-Solving Methods : Understanding, Description, Development and Resue

Understanding, Description, Development and Resue

At a Glance

Paperback

Shipping

How to return your order

Defective items

You Can Find This Book In

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