L. Console et al., USING COMPILED KNOWLEDGE TO GUIDE AND FOCUS ABDUCTIVE DIAGNOSIS, IEEE transactions on knowledge and data engineering, 8(5), 1996, pp. 690-706
Citations number
58
Categorie Soggetti
Information Science & Library Science","Computer Sciences, Special Topics","Engineering, Eletrical & Electronic","Computer Science Artificial Intelligence
Several artificial intelligence architectures acid systems based on ''
deep'' models of a domain have been proposed, iii particular for the d
iagnostic task. These systems have several advantages over traditional
knowledge based systems, but they have a main limitation in their com
putational complexity. One of the ways to face this problem is to rely
on a knowledge compilation phase, which produces knowledge that can b
e used more effectively with respect to the original one. In this pape
r we show how a specific knowledge compilation approach can focus reas
oning in abductive diagnosis, and, in particular, can improve the perf
ormances of AID, an abductive diagnosis system. The approach aims at f
ocusing the overall diagnostic cycle in two interdependent ways: avoid
ing the generation of candidate solutions to be discarded a posteriori
and integrating the generation of candidate solutions with discrimina
tion among different candidates. Knowledge compilation is used offline
to produce operational (i.e., easily evaluated) conditions that embed
the abductive reasoning strategy and are used in addition to the orig
inal model, with the goal of ruling out parts of the search space or f
ocusing on parts of it. The conditions are useful to solve most cases
using less time for computing the same solutions, yet preserving all t
he power of the model-based system for dealing with multiple faults an
d explaining the solutions. Experimental results showing the advantage
s of the approach are presented.