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 €