Distributed First Order Logic

Distributed First Order Logic (DFOL) has been introduced more than ten years ago with the purpose of formalising distributed knowledge-based systems, where knowledge about heterogeneous domains is scattered into a set of interconnected modules. DFOL formalises the knowledge contained in each module by means of first-order theories, and the interconnections between modules by means of special inference rules called bridge rules. Despite their restricted form in the original DFOL formulation, bridge rules have influenced several works in the areas of heterogeneous knowledge integration, modular knowledge representation, and schema/ontology matching. This, in turn, has fostered extensions and modifications of the original DFOL that have never been systematically described and published. This paper tackles the lack of a comprehensive description of DFOL by providing a systematic account of a completely revised and extended version of the logic, together with a sound and complete axiomatisation of a general form of bridge rules based on Natural Deduction. The resulting DFOL framework is then proposed as a clear formal tool for the representation of and reasoning about distributed knowledge and bridge rules

Reasoning with Contexts in Description Logics

Harmelen, F.A.H. van [Promotor]Schlobach, K.S. [Copromotor

Modelling (Un)Bounded Beliefs

. This paper is concerned with providing a context based logic (language + semantics) for the representation of agents's beliefs. While different approaches that make use of a single theory have been proposed in order to model agent's beliefs, such as modal logics, these often suffer from problems, as lack of modularity, logical omniscence, and dissimilarity with implementations. A partial solution to these problems is to distribute the agent's knowledge into different and separated modules which interact each others. Our approach is to provide these modules, but in the form of (multi) contexts, each one with its own local language and semantics, and to model the relations among modules as compatibility relations among contexts. We extend here this approach to capture important aspects of "ideal" agents, namely their logically omniscent nature, and of "real" agents, namely their non logically omniscent nature due to some resource-boundedness. The logic we use is based on ..