RUTCOR Colloquia - October 7, 2004
Speaker: Kazuhisa Makino
Affiliation: Osaka University, Japan
Title: On Computing all Abductive Explanations from a Propositional Horn Theory
Time: 1:30 - 2:30 PM
Location: RUTCOR Building - Room 139, Rutgers University, Busch Campus,
Piscataway, NJ
Abstract: Abduction is a fundamental mode of reasoning, which has
applications in many areas of AI and Computer Science. The computation of
abductive explanations is an important computational problem, which is at
the core of early systems such as the ATMS and Clause Management Systems,
and is intimately related to prime implicate generation in propositional
logic.
In this talk, we consider the problem of computing multiple explanations,
and in particular all explanations for an abductive query from a
propositional Horn theory. Our study pays particular attention to the
form of the query, ranging from a literal to a compound formula, to
whether explanations are based on a set of abducible literals, and to the
representation of the Horn theory, either by a Horn CNF or model-based in
terms of its characteristic models. For all these combinations, we present
either tractability results in terms of polynomial total-time algorithms,
intractability results in terms of nonexistence of such algorithms (unless
P=NP), or semi-tractability results in terms of solvability in
quasi-polynomial time, established by polynomial-time equivalence to the
problem of dualizing a monotone conjunctive normal form expression. Our
results complement previous results in the literature, and refute a
longstanding conjecture by Selman and Levesque. They elucidate the
complexity of generating all abductive explanations, and shed light on the
related problems such as generating sets of restricted prime implicates of
a Horn theory. The algorithms for tractable cases can be readily applied
for generating a polynomial subset of explanations in polynomial time.
(Joint work with Thomas Eiter)
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