Abducing Priorities to Derive Intended Conclusions
Abducing Priorities to Derive Intended Conclusions
Katsumi Inoue and Chiaki Sakama
Proceedings of the 16th International Joint Conference
on Artificial Intelligence (IJCAI-99), pp.44-49, Morgan Kaufmann, 1999.
Abstract
We introduce a framework for finding preference information
to derive desired conclusions in nonmonotonic reasoning.
A new abductive framework called preference abduction enables us
to infer an appropriate set of priorities to explain the given observation
skeptically, thereby resolving the multiple extension problem
in the answer set semantics for extended logic programs.
Preference abduction is also combined with a usual form of abduction
in abductive logic programming, and has
applications such as specification of rule preference in legal reasoning
and preference view update.
The issue of learning abducibles and priorities is also discussed,
in which abduction to a particular cause is equivalent to
abduction to preference.
Full Paper (pdf 118K)