Workshop description
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)
Short description:
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.
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