An exploratory survey of logic-based formalisms for spatial information

This chapter presents a tentative survey of logic-based formalisms for representing various aspects of spatial information ranging from the expression of spatial relationships between regions to the attribution of properties to definite regions. The first main part of the paper reviews the logic-based representations of mereotopologies in classical or modal logics, and in fuzzy and rough sets settings, as well as modal logic representations of geometries. The second main part is devoted to the handling of properties associated to regions. The association either relates properties to a current region of interest, or to explicitly named regions. Properties may be attached to a whole region and hold "everywhere", or hold "somewhere", or "elsewhere". Properties and their localization may be also pervaded with uncertainty. This overview reveals that the many existing formalisms address different issues, and when they deal with the same issue they do it differently. However, it seems that in practice there is a need for a combination of representational capabilities, which could cover both spatial relationships and localized properties, possibly in presence of uncertainty. © 2010 Springer-Verlag Berlin Heidelberg

How Potential BLFs Can Help to Decide under Incomplete Knowledge

International audienceIn a Bipolar Leveled Framework (BLF) [7], the comparison of two candidates is done on the basis of the decision principles and inhibitions which are validated given the available knowledge-bases associated with each candidate. This article defines a refinement of the rules for comparing candidates by using the potential-BLFs which can be built according to what could additionally be learned about the candidates. We also propose a strategy for selecting the knowledge to acquire in order to better discriminate between candidates

Group decision making in a bipolar leveled framework

International audienceWe study the use of a bipolar decision structure called BLF (bipolar leveled framework) in the context of collective decision making where the vote consists in giving factual information about a candidate which the group should accept or reject. A BLF defines the set of possible decision principles that may be used in order to evaluate the admissibility of a given candidate. A decision principle is a rule that relates some observations about the candidate to a given goal that the selection of this candidate may achieve or miss. The decision principles are ordered accordingly to the importance of the goal they support. Oppositions to decision principles are also described in the BLF under the form of observations that contradict the realization of the decision principles. We show how the use of a common BLF may reduce the impact of manipulation strategies in the context of group decision making

On the Dynamics of Structured Argumentation:Modeling Changes in Default Justification Logic

This paper studies information changes in default justification logic with argumentation semantics. We introduce dynamic operators that combine belief revision and default theory tools to define both prioritized and non-prioritized operations of contraction, expansion and revision for justification logic-based default theories. This combination enriches both default logics and belief revision techniques. We argue that the kind of attack called "undermining" amounts to those operations that contract a knowledge base by an attacked formula