Acquiring (Ir)relevance Knowledge for Problem solving

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Citation: Alon Y. Halevy and Yumi Iwasaki and Hiroshi Motoda. (1992) Acquiring (Ir)relevance Knowledge for Problem solving. In KSL-92-46, April,1992.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Alon Y. Halevy and Yumi Iwasaki and Hiroshi Motoda
title Acquiring (Ir)relevance Knowledge for Problem solving
number KSL-92-46
institution Knowledge Systems, AI Laboratory
year 1992
month April
Bibtex more
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abstract A major drawback of artificial intelligence systems that rely on declarative representations is that the efficiency of reasoning degrades quickly as the size of the knowledge base increases. To address this problem when building a system, we need to acquire not only knowledge about the domain, but also knowledge about the control of reasoning. In this paper, we discuss one type of such control knowledge, namely, relevance of our knowledge to specific problem solving goals. We show how this knowledge can be used by the problem solver either to ignore part of its knowledge or to automatically create abstractions and how the system can guide the acquisition of such knowledge.We ground our discussion in a framework in which knowledge about relevance can be stated, reasoned with and analyzed. We apply the framework to the problem of modeling physical devices, where creating abstractions for a given task is crucial in order to perform effective problem solving.

KSL Technical Report ID: KSL-92-46
Facts about Acquiring (Ir)relevance Knowledge for Problem solvingRDF feed
Abstract A major drawback of artificial intelligenc … A major drawback of artificial intelligence systems that rely on declarative representations is that the efficiency of reasoning degrades quickly as the size of the knowledge base increases. To address this problem when building a system, we need to acquire not only knowledge about the domain, but also knowledge about the control of reasoning. In this paper, we discuss one type of such control knowledge, namely, relevance of our knowledge to specific problem solving goals. We show how this knowledge can be used by the problem solver either to ignore part of its knowledge or to automatically create abstractions and how the system can guide the acquisition of such knowledge.We ground our discussion in a framework in which knowledge about relevance can be stated, reasoned with and analyzed. We apply the framework to the problem of modeling physical devices, where creating abstractions for a given task is crucial in order to perform effective problem solving. rder to perform effective problem solving.
Author Alon Y. Halevy and Yumi Iwasaki and Hiroshi Motoda  +
Bibtype techreport  +
Has author Alon Y. Halevy and Yumi Iwasaki and Hiroshi Motoda  +
Has identifier KSL-92-46  +
Has publishing details April,1992  +
Has title Acquiring (Ir)relevance Knowledge for Problem solving  +
Has where published KSL-92-46  +
Has year 1992  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-92-46  +
Month April  +
Number KSL-92-46  +
Process note NO  +
Title Acquiring (Ir)relevance Knowledge for Problem solving  +
Year 1992  +
Retrieved from "http://tw.rpi.edu/wiki/Acquiring_%28Ir%29relevance_Knowledge_for_Problem_solving"
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