W32: Workshop on Weighted Logics for AI (WL4AI'13)
Organizers: Lluis Godo (IIIA-CSIC, Spain), Henri Prade (IRIT-CNRS, France), Guilin Qi (Southeast University, China)
Logics provide a formal basis for the study and development of applications and systems in Artificial Intelligence. In the last decades there has been an explosion of logical formalisms capable of dealing with a variety of reasoning tasks that require an explicit representation of quantitative or qualitative weights associated with classical or modal logical formulas (in a form or another). The semantics of the weights refer to a large variety of intended meanings: belief degrees, preference degrees, truth degrees, trust degrees, etc. Examples of such weighted formalisms include probabilistic or possibilistic uncertainty logics, preference logics, fuzzy description logics, different forms of weighted or fuzzy logic programs under various semantics, weighted argumentation systems, logics handling inconsistency with weights, logics for graded BDI agents, logics of trust and reputation, logics for handling graded emotions, etc. The underlying logics range from fully compositional systems, like systems of many-valued or fuzzy logic, to non-compositional ones like modal-like epistemic logics for reasoning about uncertainty, as probabilistic or possibilistic logics, or even some combination of them. The aim of this workshop is to bring together researchers to discuss about the different motivations for the use of weighted logics in AI, the different types of calculi that are appropriate for these needs, and the problems that arise when putting them at work. The authors are especially encouraged to discuss the intended semantics of the weights they use in their paper.