Lorenza Saitta

Proceedings of the Thirteenth International Conference (ICML '96)

Machine Learning

Ronald R. Yager

Bernadette Bouchon

This book contains the papers presented at the 2nd IPMU Conference, held in Urbino (Italy), on July 4-7, 1988. The theme of the conference, Management of Uncertainty and Approximate Reasoning, is at the heart of many knowledge-based systems and a number of approaches have been developed for representing these types of information. The proceedings of the conference provide, on one hand, the opportunity for researchers to have a comprehensive view of recent results and, on the other, bring to the attention of a broader community the potential impact of developments in this area for future generation knowledge-based systems. The main topics are the following: frameworks for knowledge-based systems: representation scheme, neural networks, parallel reasoning schemes; reasoning techniques under uncertainty: non-monotonic and default reasoning, evidence theory, fuzzy sets, possibility theory, Bayesian inference, approximate reasoning; information theoretical approaches; knowledge acquisition and automated learning.

2nd International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems IPMU '88. Urbino, Italy, July 4-7, 1988. Proceedings

Uncertainty and Intelligent Systems

Jean-Daniel Zucker

This book constitutes the refereed proceedings of the 6th International Symposium on Abstraction, Reformulation, and Approximation, SARA 2005, held in Airth Castle, Scotland, UK in July 2005. The 17 revised full papers and 8 extended abstracts were carefully reviewed and selected for inclusion in the book. Also included are 3 invited papers and 8 research summaries. All current aspects of abstraction, reformulation, and approximation in the context of human common-sense reasoning, problem solving, and efficiently reasoning in complex domains are addressed. Among the application fields of these techniques are automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving.

6th International Symposium, SARA 2005, Airth Castle, Scotland, UK, July 26-29, 2005, Proceedings

Abstraction, Reformulation and Approximation

Michael May

Knowledge discovery in ubiquitous environments is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, peer-to-peer networks, data stream mining, activity recognition, Web 2.0, privacy, user modelling and others. This state-of-the-art survey is the outcome of a large number of workshops, summer schools, tutorials and dissemination events organized by KDubiq (Knowledge Discovery in Ubiquitous Environments), a networking project funded by the European Commission to bring together researchers and practitioners of this emerging community. It provides in its first part a conceptual foundation for the new field of ubiquitous knowledge discovery - highlighting challenges and problems, and proposing future directions in the area of 'smart', 'adaptive', and 'intelligent' learning. The second part of this volume contains selected approaches to ubiquitous knowledge discovery and treats specific aspects in detail. The contributions have been carefully selected to provide illustrations and in-depth discussions for some of the major findings of Part I.

Challenges, Techniques, Applications

Ubiquitous Knowledge Discovery

Ramon López de Mántaras

This is the Golden Age for Artificial Intelligence. The world is becoming increasingly automated and wired together. This also increases the opportunities for AI to help people and commerce. Almost every sub field of AI had now been used in substantial applications. Some of the fields highlighted in this publication are: CBR Technology; Model Based Systems; Data Mining and Natural Language Techniques. Not only does this publication show the activities, capabilities and accomplishments of the sub fields, it also focuses on what is happening across the field as a whole.

16th European Conference on Artificial Intelligence, August 22-27, 2004, Valencia, Spain : Including Prestigious Applicants [sic] of Intelligent Systems (PAIS 2004) : Proceedings

ECAI 2004

Antoine Cornuéjols

Attilio Giordana

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

Lorenza Saitta

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