Disjunctive Explanations in Abductive Logic Programming
Katsumi Inoue and Chiaki Sakama
Electronic Transactions on Artificial Intelligence 7
(Special Issue on Machine Intelligence 19), to appear.
Given some evidence or observation, abduction infers candidate
hypotheses to explain such an observation. In this paper, we consider
the case in which multiple explanations exist but we cannot select one
from them. Our approach leaves the decision indefinite, but offers a
consistent way to theory changes. A disjunctive explanation
which is a disjunction of possible alternative explanations, is introduced
for this purpose. For abduction, such a disjunction is useful when
obtained explanations are incorporated or assimilated into the current
knowledge base. The assimilated knowledge base preserves the intended
semantics from the collection of all possible updated knowledge bases.
The merit of disjunctive explanations is that we only need one current
knowledge base at a time, still keeping every possible change in a
single state. This method is extended to accomplish an update when
there are multiple ways to remove old unnecessary hypotheses from the
knowledge base. The proposed framework is well applicable to view
updates in disjunctive databases.
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