This book constitutes the refereed proceedings of the 9th International Conference on Discovery Science, DS 2006, held in Barcelona, Spain in October 2006, co-located with the 17th International Conference on Algorithmic Learning Theory, ALT 2006.

The 23 revised long papers and the 18 revised regular papers presented together with five invited papers were carefully reviewed and selected from 87 submissions.

The lives of people all around the world, especially in industrialized nations, continue to be changed by the presence and growth of the Internet. Its in?uence is felt at scales ranging from private lifestyles to national economies, boosting thepaceatwhichmoderninformationandcommunicationtechnologiesin?uence personal choices along with business processes and scienti?c endeavors. In addition to its billions of HTML pages, the Web can now be seen as an open repository of computing resources. These resources provide access to computational services as well as data repositories, through a rapidly growing variety of Web applications and Web services. However, people's usage of all these resources barely scratches the surface of the possibilities that such richness should o?er. One simple reason is that, given the variety of information available and the rate at which it is being extended, it is di?cult to keep up with the range of resources relevant to one's interests. Another reason is that resources are o?ered in a bewildering variety of formats and styles, so that many resources e?ectively stand in isolation. This is reminiscent of the challenge of enterprise application integration, - miliar to every large organization be it in commerce, academia or government. Thechallengearisesbecauseoftheaccumulationofinformationandcommuni- tion systems over decades, typically without the technical provision or political will to make them work together. Thus the exchange of data among those s- tems is di?cult and expensive, and the potential synergetic e?ects of combining them are never realized.

This volume contains the papers presented at the 14th Annual Conference on Algorithmic Learning Theory (ALT 2003), which was held in Sapporo (Japan) duringOctober17-19,2003. Themainobjectiveoftheconferencewastoprovide an interdisciplinary forum for discussing the theoretical foundations of machine learning as well as their relevance to practical applications. The conference was co-locatedwiththe6thInternationalConferenceonDiscoveryScience(DS2003). The volume includes 19 technical contributions that were selected by the program committee from 37 submissions. It also contains the ALT 2003 invited talks presented by Naftali Tishby (Hebrew University, Israel) on "E?cient Data Representations that Preserve Information," by Thomas Zeugmann (University of Lub ] eck, Germany) on "Can Learning in the Limit be Done E?ciently?," and by Genshiro Kitagawa (Institute of Statistical Mathematics, Japan) on "S- nal Extraction and Knowledge Discovery Based on Statistical Modeling" (joint invited talk with DS 2003). Furthermore, this volume includes abstracts of the invitedtalksforDS2003presentedbyThomasEiter(ViennaUniversityofTe- nology, Austria) on "Abduction and the Dualization Problem" and by Akihiko Takano (National Institute of Informatics, Japan) on "Association Computation for Information Access. " The complete versions of these papers were published in the DS 2003 proceedings (Lecture Notes in Arti?cial Intelligence Vol. 2843). ALT has been awarding theE. MarkGoldAward for the most outstanding paper by a student author since 1999. This year the award was given to Sandra Zilles for her paper "Intrinsic Complexity of Uniform Learning. " This conference was the 14th in a series of annual conferences established in 1990. ContinuationoftheALTseriesissupervisedbyitssteeringcommittee, c- sisting of: Thomas Zeugmann (Univ."

This book constitutes the refereed proceedings of the 4th International Conference on Discovery Science, DS 2001, held in Washington, DC, USA, in November 2001.The 30 revised full papers presented together with 3 invited papers and 11 posters were carefully reviewed and selected from numerous submissions. Among the topics addressed in their relation to discovery science are inference, algorithmic learning, heuristic search, database management, data mining, inductive logic programming, information agents, information retrieval, information visualization, etc.

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in October 1995.
The book contains 21 revised full papers selected from 46 submissions together with three invited contributions. It covers all current areas related to algorithmic learning theory, in particular the theory of machine learning, design and analysis of learning algorithms, computational logic aspects, inductive inference, learning via queries, artificial and biologicial neural network learning, pattern recognition, learning by analogy, statistical learning, inductive logic programming, robot learning, and gene analysis.

This book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation.
The contributions by 11 participants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory.

This volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.)
The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.

This volume contains the papers that were presented at the Third Workshop onAlgorithmic Learning Theory, held in Tokyo in October 1992. In addition to 3invited papers, the volume contains 19 papers accepted for presentation, selected from 29 submitted extended abstracts. The ALT workshops have been held annually since 1990 and are organized and sponsored by the Japanese Society for Artificial Intelligence. The main objective of these workshops is to provide an open forum for discussions and exchanges of ideasbetween researchers from various backgrounds in this emerging, interdisciplinary field of learning theory. The volume is organized into parts on learning via query, neural networks, inductive inference, analogical reasoning, and approximate learning.

This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whose focus is on theories of machine learning and the application of such theories to real-world learning problems. The ALT workshops have been held annually since 1990, sponsored by the Japanese Society for Artificial Intelligence. The volume is organized into parts on inductive logic and inference, inductive inference, approximate learning, query learning, explanation-based learning, and new learning paradigms.

This proceedings volume contains a selection of revised and extended papers presented at the Second International Workshop on Nonmonotonic and InductiveLogic, NIL '91, which took place at Reinhardsbrunn Castle, December 2-6, 1991. The volume opens with an extended version of a tutorial on nonmonotonic logic by G. Brewka, J. Dix, and K. Konolige. Fifteen selected papers follow, on a variety of topics. The majority of papers belong either to the area of nonmonotonic reasoning or to the field of inductive inference, but some papers integrate research from both areas. The first workshop in this series was held at the University of Karlsruhe in December 1990 and its proceedings were published as Lecture Notes in Artificial Intelligence Volume 543. The series of workshops was made possible by financial support from Volkswagen Stiftung, Hannover. This workshop was also supported by IBM Deutschland GmbH and Siemens AG.

This volume contains the text of the five invited papers and 16 selected contributions presented at the third International Workshop on Analogical and Inductive Inference, AII 92, held in Dagstuhl Castle, Germany, October 5-9, 1992. Like the two previous events, AII '92 was intended to bring together representatives from several research communities, in particular, from theoretical computer science, artificial intelligence, and from cognitive sciences. The papers contained in this volume constitute a state-of-the-art report on formal approaches to algorithmic learning, particularly emphasizing aspects of analogical reasoning and inductive inference. Both these areas are currently attracting strong interest: analogical reasoning plays a crucial role in the booming field of case-based reasoning, and, in the fieldof inductive logic programming, there have recently been developed a number of new techniques for inductive inference.

This proceedings volume contains revised and reviewed papers based on talks presented at the first International Workshop on Nonmonotonic and Inductive Logic held in Karlsruhe, December 1990. The workshop was supported by the Volkswagen-Stiftung, Hannover, and provided a forum for researchers from the two fields to communicate and find areas of cooperation. The papersare organized into sections on: - Nonmonotonicity in logic programs - Axiomatic approach to nonmonotonic reasoning - Inductive inference - Autoepistemic logic - Belief updates The bulk of the papers are devoted to nonmonotonic logic and provide an up-to-date view of the current state of research presented by leading experts in the field. A novelty in the contributions from the area of inductive logic is the analysis of nonmonotonicity in the theory of inductive learning.

The algebraic specification of abstract data types is now a well establishedresearch topic in computer science. This area influences both applications and theoretical foundations of methodologies which support the design and formal development of reliable software. The Seventh Workshop on Specification of Abstract Data Types took place in Wusterhausen/Dosse, April17-20, 1990, and was organized in cooperation with the ESPRIT Basic Research Working Group COMPASS. The main topics covered by the workshop were: - Modularization - Object orientation - Higher-order types anddependent types - Inductive completion - Algebraic high-level nets.

This volume contains revised versions of presentations at the International Workshop on Analogical and Inductive Inference (AII '86) held in Wendisch-Rietz, GDR, October 16-10, 1986. Inductive inference and analogical reasoning are two basic approaches to learning algorithms. Both allow for exciting problems and promising concepts of invoking deeper mathematical results for considerable advances in intelligent software systems. Hence analogical and inductive inference may be understood as a firm mathematical basis for a large variety of problems in artificial intelligence. While the papers on inductive inference contain technical results and reflect the state of the art of this well-developed mathematical theory, those devoted to analogical reasoning reflect the ongoing process of developing the basic concepts of the approach. The workshop thus contributes significantly to the advancement of this field.