Prioritized Knowledge Bases Research Articles

Modular orders on defaults in formal argumentation

Abstract Default logic and formal argumentation are paradigmatic methods in the study of nonmonotonic inference. Defeasible information often comes in different strengths stemming from different degrees of reliability in epistemic applications or from varying strengths of authorities issuing norms in deontic applications. In both paradigms, methods have been developed to deal with prioritized knowledge bases and normative systems. Questions of comparability of these methods therefore naturally arise. Argumentation theory has been developed with a strong emphasis on unification. It is therefore a desideratum to obtain natural representations of various approaches to (prioritized) default logic within frameworks of structured argumentation, such as ASPIC. Important steps in this direction have been presented in Liao et al. (2016, 2018). In this work, we identify and address some problems in earlier translations, we broaden the focus from total to modular orderings of defaults and we consider non-normal defaults.

Measuring the blame of each formula for inconsistent prioritized knowledge bases

It is increasingly recognized that identifying the degree of blame or responsibility of each formula for inconsistency of a knowledge base (i.e. a set of formulas) is useful for making rational decisions to resolve inconsistency in that knowledge base. Most current techniques for measuring the blame of each formula with regard to an inconsistent knowledge base focus on classical knowledge bases only. Proposals for measuring the blames of formulas with regard to an inconsistent prioritized knowledge base have not yet been given much consideration. However, the notion of priority is important in inconsistency-tolerant reasoning. This article investigates this issue and presents a family of measurements for the degree of blame of each formula in an inconsistent prioritized knowledge base by using the minimal inconsistent subsets of that knowledge base. First of all, we present a set of intuitive postulates as general criteria to characterize rational measurements for the blames of formulas of an inconsistent prioritized knowledge base. Then we present a family of measurements for the blame of each formula in an inconsistent prioritized knowledge base under the guidance of the principle of proportionality, one of the intuitive postulates. We also demonstrate that each of these measurements possesses the properties that it ought to have. Finally, we use a simple but explanatory example in requirements engineering to illustrate the application of these measurements. Compared to the related works, the postulates presented in this article consider the special characteristics of minimal inconsistent subsets as well as the priority levels of formulas. This makes them more appropriate to characterizing the inconsistency measures defined from minimal inconsistent subsets for prioritized knowledge bases as well as classical knowledge bases. Correspondingly, the measures guided by these postulates can intuitively capture the inconsistency for prioritized knowledge bases.

Measuring conflict and agreement between two prioritized knowledge bases in possibilistic logic

In this paper we investigate the relationship between two prioritized knowledge bases by measuring both the conflict and the agreement between them. First of all, a quantity of conflict and two quantities of agreement are defined. The former is shown to be a generalization of the well-known Dalal distance which is the Hamming distance between two interpretations. The latter are, respectively, a quantity of strong agreement which measures the amount of information on which two belief bases “totally” agree, and a quantity of weak agreement which measures the amount of information that is believed by one source but is unknown to the other. All three quantity measures are based on the weighted prime implicant, which represents beliefs in a prioritized belief base. We then define a degree of conflict and two degrees of agreement based on our quantity of conflict and quantities of agreement. We also consider the impact of these measures on belief merging and information source ordering.

Dynamic evolutions based on ontologies

With the continuous changes in application requirements of the enterprises, Web resources must be updated, so do the underlying ontologies that are associated with the Web resources. In the situation, it is very challenging for ontological engineers to specify the changes of ontologies, keep their consistencies and achieve semantic query of Web resources based on the evolving ontologies. We propose a construct called Prioritized Knowledge Base ( PKB ) based on SHOQ( D) description logic, and discuss some properties of PKB . PKB can be used for describing the evolutions and updates of ontologies with conflicting information. Furthermore, we develop some algorithms for checking conflict rules and performing semantic query based on PKB .

A Practical Approach to Revising Prioritized Knowledge Bases.

This paper investigates simple syntactic methods to revise prioritized belief bases, that are semantically meaningful in the frameworks of possibility theory and of Spohn's (1988) ordinal conditional functions. Here, revising prioritized belief bases amounts to conditioning a distribution function on interpretations. Different types of scales for priorities are discussed: finite vs. infinite, numerical vs. ordinal. Syntactic revision is envisaged as a process which transforms prioritized belief bases into a new prioritized belief base, and thus allows for the subsequent iteration.