Title :
The role of explanatory relationships in strategies for abduction
Author :
Tanner, Michael C. ; Josephson, John R.
Author_Institution :
George Mason Univ., Fairfax, VA, USA
fDate :
6/1/1994 12:00:00 AM
Abstract :
We conducted an experiment to test whether explicitly represented knowledge of explanatory relationships can significantly reduce uncertainty and increase correctness in the abductive reasoning process. We compared the performance of four abduction machines, each using a different combination of knowledge types and a different reasoning strategy on an existing knowledge base with three kinds of knowledge: routine-recognition knowledge (precompiled knowledge for pattern-based hypothesis scoring); hypothesis-incompatibility knowledge (two hypotheses cannot both be true); and knowledge of explanatory relationships between hypotheses and data items. We conclude that knowledge-based diagnostic systems can improve their accuracy by explicitly explaining data in addition to the usual pattern-based hypothesis scoring.<>
Keywords :
explanation; heuristic programming; inference mechanisms; knowledge representation; uncertainty handling; abduction machines; abduction strategies; abductive reasoning process; accuracy; correctness; data items; explanatory relationships; explicitly represented knowledge; hypothesis-incompatibility knowledge; knowledge base; knowledge types; knowledge-based diagnostic systems; pattern-based hypothesis scoring; performance; precompiled knowledge; reasoning strategy; routine-recognition knowledge; uncertainty; Assembly; Blood; Design for experiments; Diseases; Knowledge based systems; Medical diagnosis; Pattern matching; Permission; System testing; Uncertainty;
Journal_Title :
IEEE Expert
