This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.

Thisvolumecontainstheproceedingsofthe?rstthreeworkshopsonUncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007. In addition to revised and stronglyextendedversionsofselectedworkshoppapers, wehaveincludedinvited contributions from leading experts in the ?eld and closely related areas. With this, the present volume represents the ?rst comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the Semantic Web, capturing di?erent models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge. TheWorldWide Web communityenvisionse?ortless interactionbetween- mansandcomputers, seamlessinteroperabilityandinformationexchangeamong Webapplications, andrapidandaccurateidenti?cationandinvocationofapp- priate Web services.As workwith semantics and servicesgrowsmoreambitious, there is increasing appreciation of the need for principled approaches to the f- mal representation of and reasoning under uncertainty. The term uncertainty is intended here to encompass a variety of forms of incomplete knowledge, - cluding incompleteness, inconclusiveness, vagueness, ambiguity, and others. The termuncertaintyreasoning ismeanttodenotethefullrangeofmethodsdesigned for representing and reasoning with knowledge when Boolean truth values are unknown, unknowable, or inapplicable. Commonly applied approachesto unc- tainty reasoning include probability theory, Dempster-Shafer theory, fuzzy logic and possibility theory, and numerous other methodologies. A few Web-relevant challenges which are addressed by reasoning under - certainty include: Uncertaintyofavailableinformation: MuchinformationontheWorldWide Web is uncertain. Examples include weather forecasts or gambling odds. Canonical methods for representing and integrating such information are necessaryforcommunicating it ina seamlessfashi

This volume contains the postproceedings of the 1st International Workshop on Computational Autonomy - Potential, Risks, Solutions (AUTONOMY 2003), held at the 2nd International Joint Conference on Autonomous Agents and Multi-agentSystems(AAMAS2003),July14,2003,Melbourne,Australia.Apart from revised versions of the accepted workshop papers, we have included invited contributions from leading experts in the ?eld. With this, the present volume represents the ?rst comprehensive survey of the state-of-the-art of research on autonomy, capturing di?erent theories of autonomy, perspectives on autonomy in di?erent kinds of agent-based systems, and practical approaches to dealing with agent autonomy. Agent orientation refers to a software development perspective that has evolved in the past 25 years in the ?elds of computational agents and multiagent systems. The basic notion underlying this perspective is that of a computational agent, that is, an entity whose behavior deserves to be called ?exible, social, and autonomous. As an autonomous entity, an agent possesses action choice and is at least to some extent capable of deciding and acting under self-control. Through its emphasis on autonomy, agent orientation signi?cantly di?ers from traditional engineering perspectives such as structure orientation or object o- entation. These perspectives are targeted on the development of systems whose behavior is fully determined and controlled by external units (e.g., by a p- grammer at design time and/or a user at run time), and thus inherently fail to capture the notion of autonomy.

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2011, 2012, and 2013. The 16 papers presented were carefully reviewed and selected from numerous submissions. The papers included in this volume are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.