32 research outputs found
Abductive and Consistency-Based Diagnosis Revisited: a Modeling Perspective
Diagnostic reasoning has been characterized logically as consistency-based
reasoning or abductive reasoning. Previous analyses in the literature have
shown, on the one hand, that choosing the (in general more restrictive)
abductive definition may be appropriate or not, depending on the content of the
knowledge base [Console&Torasso91], and, on the other hand, that, depending on
the choice of the definition the same knowledge should be expressed in
different form [Poole94].
Since in Model-Based Diagnosis a major problem is finding the right way of
abstracting the behavior of the system to be modeled, this paper discusses the
relation between modeling, and in particular abstraction in the model, and the
notion of diagnosis.Comment: 5 pages, 8th Int. Workshop on Nonmonotonic Reasoning, 200
Recycling Computed Answers in Rewrite Systems for Abduction
In rule-based systems, goal-oriented computations correspond naturally to the
possible ways that an observation may be explained. In some applications, we
need to compute explanations for a series of observations with the same domain.
The question whether previously computed answers can be recycled arises. A yes
answer could result in substantial savings of repeated computations. For
systems based on classic logic, the answer is YES. For nonmonotonic systems
however, one tends to believe that the answer should be NO, since recycling is
a form of adding information. In this paper, we show that computed answers can
always be recycled, in a nontrivial way, for the class of rewrite procedures
that we proposed earlier for logic programs with negation. We present some
experimental results on an encoding of the logistics domain.Comment: 20 pages. Full version of our IJCAI-03 pape
Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach
Matchmaking arises when supply and demand meet in an electronic marketplace,
or when agents search for a web service to perform some task, or even when
recruiting agencies match curricula and job profiles. In such open
environments, the objective of a matchmaking process is to discover best
available offers to a given request. We address the problem of matchmaking from
a knowledge representation perspective, with a formalization based on
Description Logics. We devise Concept Abduction and Concept Contraction as
non-monotonic inferences in Description Logics suitable for modeling
matchmaking in a logical framework, and prove some related complexity results.
We also present reasonable algorithms for semantic matchmaking based on the
devised inferences, and prove that they obey to some commonsense properties.
Finally, we report on the implementation of the proposed matchmaking framework,
which has been used both as a mediator in e-marketplaces and for semantic web
services discovery