Reasoning about Temporal Context using Ontology and Abductive Constraint Logic Programming

The underlying assumptions for interpreting the meaning of data often change over time, which further complicates the problem of semantic heterogeneities among autonomous data sources. As an extension to the COntext INterchange (COIN) framework, this paper introduces the notion of temporal context as a formalization of the problem. We represent temporal context as a multi-valued method in F-Logic; however, only one value is valid at any point in time, the determination of which is constrained by temporal relations. This representation is then mapped to an abductive constraint logic programming framework with temporal relations being treated as constraints. A mediation engine that implements the framework automatically detects and reconciles semantic differences at different times. We articulate that this extended COIN framework is suitable for reasoning on the Semantic Web.Singapore-MIT Alliance (SMA

Selection-free predictions in global games with endogenous information and multiple equilibria

Global games with endogenous information often exhibit multiple equilibria. In this paper, we show how one can nevertheless identify useful predictions that are robust across all equilibria and that cannot be delivered in the common-knowledge counterparts of these games. Our analysis is conducted within a flexible family of games of regime change, which have been used to model, inter alia, speculative currency attacks, debt crises, and political change. The endogeneity of information originates in the signaling role of policy choices. A novel procedure of iterated elimination of nonequilibrium strategies is used to deliver probabilistic predictions that an outside observer—an econometrician—can form under arbitrary equilibrium selections. The sharpness of these predictions improves as the noise gets smaller, but disappears in the complete-information version of the model

Belief revision for deductive databases

We study the problem of evolution of knowledge in a deductive database by formulating this in terms of belief revision and using abductive (hypothetical) reasoning. The problem of the existence of multiple different solutions to update requests on such databases is addressed by imposing naturally related preference orderings on these and choosing the preferable ones. This has highlighted and clarified further the role on integrity constraints in updating databases. A Truth Maintenance system associated to our abductive approach to belief revision is developed for recovering from wrong choices of solutions in the light of new information and for maintaining consistency as the database evolves