The main objective of the project is the study and development of efficient systems that allow to extract information in the context of knowledge bases or sources that contain incomplete, vague or inconsistent information. From the theoretical point of view, we intend to advance in the study of appropriate logics to describe vague and uncertain information, mainly t-norm based fuzzy logics and modal extensions to reason about graded preferences and uncertainty, and fuzzy description logics as terminological knowledge representation languages involving fuzzy concepts and relations. On the other hand, we intend to advance the study of efficient systems for reasoning problems (e.g. consequence, subsumption) for these logics. these sources. In problems with inconsistent informa- tion, usual reasoning procedures can reach contradictory conclusions. So one of our goals is also to deepen in the application and development of logical argumentative models which present to the end user justified or warranted conclusions, and to extend these models to distributed environments, where knowledge is distributed between different agents. To limit the maximum response time of the reasoning systems we will also examine the application of efficient transformations based on the problems of satisfiability and maximum satisfiability, for which there are highly efficient algorithms. Finally, we will study the use of reasoning systems studied and developed in different application do- mains, such as effective reasoning in a graded BDI agent architecture, optimization with preferences, decision support in medical diagnosis and management of online political discussions.