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A Mechanism for Inferring Approximate Solutions Under Incomplete Knowledge Based on Rule Similarity by Viet Ha Nguyen; Tsutomu Ishikawa; Akinori Abe is a Computer Science article available to read on EtoBox.

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## Abstract This paper proposes an inference method which can obtain an approximate solution even if the knowledge stored in the problem‐solving system is incomplete. When a rule needed for solving the problem does not exist, the problem can be solved by using rules similar to the existing rules. In an implementation using SLD procedure, a resolution is executed between a subgoal and a rule if an atom of the subgoal is similar to the consequence atom of the rule. Similarities between atoms are calculated using a knowledge base of words with account of the reasoning situation, and the reliability of the derived solution is calculated based on these similarities. If many solutions are obtained, they are grouped into classes of similar solutions and a representative solution is then selected for each class. The proposed method was verified experimentally by solving simple problems. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(9): 78–89, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1154

Who reads A Mechanism for Inferring Approximate Solutions Under Incomplete Knowledge Based on Rule Similarity?

Author

Viet Ha Nguyen; Tsutomu Ishikawa; Akinori Abe

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0882-1666)

Published

2002

Language

EN

Field

Computer Science (Physical Sciences)