WL4AI 2013 - 2013 Workshop on Weighted Logics for AI: logic, uncertain beliefs, preferences, partial truth

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. Any paper on a weighted logic in relation to any of the following topics (but not limited to) with an AI perspective is welcome:
* argumentation systems
* belief revision
* description logic
* graded BDI agents
* graded emotions
* graded truth
* inconsistency handling
* information fusion
* logic programs
* non monotonic reasoning
* preference modeling
* trust and reputation
* uncertainty
Authors are especially encouraged to discuss the intended semantics of the weights they use in their paper.