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ARINF

Efficient automated reasoning systems with incomplete and imprecise information based on SAT and CSP
gen. 2010 - des. 2012
Investigador principal
External Researchers
C. Chesñevar,Pilar Dellunde,
Project description
The main goal of the project is the study and development of efficient systems, able to cope with information from knowledge sources consisting on incomplete information, hence, inconsistent and vague information. On the one hand, we want to investigate the proper logics to describe such information types, mainly t-norm based logics and fuzzy extensions for description logics. On the other hand, we will study efficient systems for automatic reasoning, able to infer valid information from the above mentioned knowledge sources. As the obtained information may be inconsistent, the reasoning procedures may conclude on wrong information, so, an objective will be to study the application and development of argumentative models, able to justify the soundness of the obtained conclusions in front of the final user. In order to bound the response time of the reasoning system we will explore efficient transformations based on satisfiability and maximum satisfiability problems, that already have highly efficient solving algorithms. Finally, through the worst-case and typical complexity study, we will bound the solving hardness for some particular reasoning problems. For typical case complexity, we will employ either generators of synthetic problems, or real problems obtained from semantic web ontologies but including uncertainty and vagueness in the information encoded, according to the studied fuzzy description logics of this project. Funding: 81.554 €