Combining open and closed world reasoning for the semantic web

Dissertação para obtenção do Grau de Doutor em InformáticaOne important problem in the ongoing standardization of knowledge representation languages for the Semantic Web is combining open world ontology languages, such as the OWL-based ones, and closed world rule-based languages. The main difficulty of such a combination is that both formalisms are quite orthogonal w.r.t. expressiveness and how decidability is achieved. Combining non-monotonic rules and ontologies is thus a challenging task that requires careful balancing between expressiveness of the knowledge representation language and the computational complexity of reasoning. In this thesis, we will argue in favor of a combination of ontologies and nonmonotonic rules that tightly integrates the two formalisms involved, that has a computational complexity that is as low as possible, and that allows us to query for information instead of calculating the whole model. As our starting point we choose the mature approach of hybrid MKNF knowledge bases, which is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. We extend the two-valued framework of MKNF logics to a three-valued logics, and we propose a well-founded semantics for non-disjunctive hybrid MKNF knowledge bases. This new semantics promises to provide better efficiency of reasoning,and it is faithful w.r.t. the original two-valued MKNF semantics and compatible with both the OWL-based semantics and the traditional Well- Founded Semantics for logic programs. We provide an algorithm based on operators to compute the unique model, and we extend SLG resolution with tabling to a general framework that allows us to query a combination of non-monotonic rules and any given ontology language. Finally, we investigate concrete instances of that procedure w.r.t. three tractable ontology languages, namely the three description logics underlying the OWL 2 pro les.Fundação para a Ciência e Tecnologia - grant contract SFRH/BD/28745/200

Foundations of Empirical Software Engineering: The Legacy of Victor R. Basili

This book captures the main scientific contributions of Victor R. Basili, who has significantly shaped the field of empirical software engineering from its very start. He was the first to claim that software engineering needed to follow the model of other physical sciences and develop an experimental paradigm. By working on this postulate, he developed concepts that today are well known and widely used, including the Goal-Question-Metric method, the Quality-Improvement paradigm, and the Experience Factory. He is one of the few software pioneers who can aver that their research results are not just scientifically acclaimed but are also used as industry standards. On the occasion of his 65th birthday, celebrated with a symposium in his honor at the International Conference on Software Engineering in St. Louis, MO, USA in May 2005, Barry Boehm, Hans Dieter Rombach, and Marvin V. Zelkowitz, each a long-time collaborator of Victor R. Basili, selected the 20 most important research papers of their friend, and arranged these according to subject field. They then invited renowned researchers to write topical introductions. The result is this commented collection of timeless cornerstones of software engineering, hitherto available only in scattered publications

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms