Preference-Based Inconsistency Management in Multi-Context Systems

Abstract

Multi-Context Systems (MCS) are a powerful framework for interlinking possibly heterogeneous, autonomous knowledge bases, where information can be exchanged among knowledge bases by designated bridge rules with negation as failure. An acknowledged issue with MCS is inconsistency that arises due to the information exchange. To remedy this problem, inconsistency removal has been proposed in terms of repairs, which modify bridge rules based on suitable notions for diagnosis of inconsistency. In general, multiple diagnoses and repairs do exist; this leaves the user, who arguably may oversee the inconsistency removal, with the task of selecting some repair among all possible ones. To aid in this regard, we extend the MCS framework with preference information for diagnoses, such that undesired diagnoses are filtered out and diagnoses that are most preferred according to a preference ordering are selected. We consider preference information at a generic level and develop meta-reasoning techniques on diagnoses in MCS that can be exploited to reduce preference-based selection of diagnoses to computing ordinary subset-minimal diagnoses in an extended MCS. We describe two meta-reasoning encodings for preference orders: the first is conceptually simple but may incur an exponential blowup. The second is increasing only linearly in size and based on duplicating the original MCS. The latter requires nondeterministic guessing if a subset-minimal among all most preferred diagnoses should be computed. However, a complexity analysis of diagnoses shows that this is worst-case optimal, and that in general, preferred diagnoses have the same complexity as subset-minimal ordinary diagnoses. Furthermore, (subset-minimal) filtered diagnoses and (subset-minimal) ordinary diagnoses also have the same complexity.

Preference-Based Inconsistency Management in Multi-Context Systems (Extended Abstract)

“…The full article [Eiter and Weinzierl, 2017] contains formal notions, much more details, examples, and many results omitted for this abstract, including another encoding for total preference orders, and an appendix with full proofs.…”

“…The results are given in Table 1, where MCSD m is the complexity of deciding whether D ∈ D ± m (M ) holds (cf. [Eiter et al, 2014]). Notably, the problems for our extended notions of diagnosis do not have higher complexity than the basic notion, except for MCSDPH m,tmax .…”

“…The semantics of the whole system then emerges from the local semantics of the constituent knowledge bases. Practical applications of MCS are defeasible reasoning in ambient intelligence [Bikakis and Antoniou, 2010], cooperation in distributed information systems [Caire and Bikakis, 2011], and the METIS system for maritime situation awareness support [Velikova et al, 2014].…”

“…[Giunchiglia and Serafini, 1994;Ghidini and Giunchiglia, 2001;Roelofsen and Serafini, 2005;Brewka et al, 2007;Serafini and Homola, 2012;Bikakis and Antoniou, 2010;Brewka et al, 2011]). For generality, this work is based on MCS with heterogeneous contexts and nonmonotonic information exchange [Brewka and Eiter, 2007].…”

“…ceteris paribus preferences [Doyle et al, 1991], CP-nets [Boutilier et al, 2004;Domshlak et al, 2001;Goldsmith et al, 2008], or utility functions [Von Neumann and Morgenstern, 1944]. As there is no one-fits-all preference formalism our approach is based on the idea that a user-customized preference on diagnoses, specified in a formalism chosen by the user, can be seen as a knowledge-base or context of an MCS.…”