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Task modeling with reusable problem-solving methods

delete1995-12-01
delete93
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OA
AI
H
Henrik Eriksson
Y
Yuval Shaḥar
S
Samson W. Tu
Á
Ángel R. Puerta
M
Mark A. Musen
DOI:10.1016/0004-3702(94)00040-9delete
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Abstract

Abstract

En 中文
Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection (from a library), and method configuration. PROTBGB-II is a knowledge-engineering environment that allows developers to select and configure problem-solving methods. In addition, PROTBGB-II generates domain-specific knowledge-acquisition tools that domain specialists can use to create knowledge bases on which the methods may operate. The board-game method is a problem-solving method that defines control knowledge for a class of tasks that developers can model in a highly specific way. The method adopts a conceptual model of problem solving in which the solution space is construed as a ''game board'' on which the problem solver moves ''playing pieces'' according to prespecified rules. This familiar conceptual model simplifies the developer's cognitive demands when configuring the board-game method to support new application tasks. We compare configuration of the board-game method to that of a chronological-backtracking problem-solving method for the same application tasks (for example, towers of Hanoi and the Sisyphus room-assignment problem), We also examine how method designers can specialize problem-solving methods by making ontological commitments to certain classes of tasks. We exemplify this technique by specializing the chronological-backtracking method to the board-game method.
Keywords:
KNOWLEDGE-ACQUISITION
ABSTRACTION
REUSE
TOOL
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Journal

Artificial Intelligence Review cover
Artificial Intelligence Review
IF:
13.9
Papers:
6.1K
Citations:
1.9W

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