Belief revision in structured probabilistic argumentation

Abstract

Abstract. In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -based on well-known ones developed for classical knowledge bases -that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates.

Constraints and changes: A survey of abstract argumentation dynamics

“…This topic is out of the scope of the current paper, but a classification of the existing work on argumentation dynamics in structured frameworks is interesting (see e.g. Falappa et al [49], Moguillansky et al [59], Shakarian et al [64], Mailly [58] and Snaith and Reed [65]). Similarly to what is done here for abstract argumentation, this could highlight some open questions for these settings, and maybe some interesting relations with the work about abstract argumentation.…”

Delegated updates in epistemic graphs for opponent modelling

“…However, instead of removing or replacing formulas or models of a classical knowledge base, we adapt probabilities (degrees of beliefs) here. Non-prioritized belief change operations for structured probabilistic argumentation have been investigated in [70]. Knowledge bases are given as special probabilistic logic programs and the belief change task is, roughly speaking, to incorporate a new fact or rule.…”

“…This is accomplished by adapting the knowledge base similar to classical approaches. In particular, some classical axioms for (non-prioritized) belief changes are satisfied by this approach [70]. However, there is again no simple translation to our framework because we adapt an epistemic state that is represented by a probability distribution rather than by a knowledge base.…”

Resolving conflicts in knowledge for ambient intelligence

“…All the above works (and most of the works related to belief change in general) are dealing with prioritized belief change, that is, they assume that the new information is unconditionally accepted (an assumption known as the principle of primacy of new information (Dalal, 1988) or the principle of success ), and this is also captured in one of the AGM postulates. The effects of dropping this assumption were studied in the subfield of non-prioritized belief change (Hansson, 1997;Hansson et al, 2001;Shakarian et al, 2014). Non-prioritized belief change is important for the AmI setting, where the cause of a conflict may be found in the input, for example, a faulty sensor reading (sensory input conflict), and not in the agent's KB.…”