Eyke Hüllermeier - Böcker

Case-based reasoning (CBR) has received a great deal of attention in recent years and has established itself as a core methodology in the field of artificial intelligence. The key idea of CBR is to tackle new problems by referring to similar problems that have already been solved in the past. More precisely, CBR proceeds from individual experiences in the form of cases. The generalization beyond these experiences typically relies on a kind of regularity assumption demanding that 'similar problems have similar solutions'.Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR. The case-based approximate reasoning methods thus obtained especially emphasize the heuristic nature of case-based inference and aspects of uncertainty in CBR. This way, the book contributes to a solid foundation of CBR which is grounded on formal concepts and techniques from the aforementioned fields. Besides, it establishes interesting relationships between CBR and approximate reasoning, which not only cast new light on existing methods but also enhance the development of novel approaches and hybrid systems.This books is suitable for researchers and practioners in the fields of artifical intelligence, knowledge engineering and knowledge-based systems.

This book constitutes the proceedings of the 20th International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Rennes, France, during April 20-22, 2022. The 31 papers included in this book were carefully reviewed and selected from 73 submissions. They deal with high quality, novel research in intelligent data analysis.

This book constitutes the refereed proceedings of the 23rd International Conference on Case-Based Reasoning Research and Development, ICCBR 2015, held in Frankfurt am Main, Germany, in September 2015. The 26 revised full papers presented were carefully reviewed and selected from 37 submissions.

The International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the m- agement of uncertainty and aggregation of information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas th between theoreticians and practitioners working in these areas. The 13 IPMU conference took place in Dortmund, Germany, June 28-July 2, 2010. This volumecontains77papersselectedthrougharigorousreviewingprocess among 320 submissions from 36 countries. The contributions re?ect the richness of research in the ?eld of computational intelligence and represent several - portant developments, speci?cally focused on the following sub?elds: (a) machine learning, data mining, and pattern recognition, (b) uncertainty handling, (c) aggregation and fusion of information, (d) logic and knowledge processing. We were delighted that Melanie Mitchell (Portland State University, USA), NihkilR.Pal(IndianStatisticalInstitute),BernhardSch. olkopf(MaxPlanckI- titute for Biological Cybernetics, Tubing .. en, Germany) and Wolfgang Wahlster (German Research Center for Arti?cial Intelligence, Saarbruc .. ken) accepted our invitationstopresentkeynotelectures.JimBezdekreceivedtheKamp'edeF' eriet Award,grantedeverytwo yearsonthe occasionofthe IPMUconference,in view ofhiseminentresearchcontributionstothehandlingofuncertaintyinclustering, data analysis and pattern recognition.

The International Conference on Information Processing and Management of - certainty in Knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the management of uncertainty and aggregation of information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas between th theoreticians and practitioners working in these areas and related ?elds. The 13 IPMU conference took place in Dortmund, Germany, June 28–July 2, 2010. This volume contains 79 papers selected through a rigorous reviewing process. The contributions re?ect the richness of research on topics within the scope of the conference and represent several important developments, speci?cally focused on theoretical foundations and methods for information processing and management of uncertainty in knowledge-based systems. We were delighted that Melanie Mitchell (Portland State University, USA), Nihkil R. Pal (Indian Statistical Institute), Bernhard Sch¨ olkopf (Max Planck I- titute for Biological Cybernetics, Tubing ¨ en, Germany) and Wolfgang Wahlster (German Research Center for Arti?cial Intelligence, Saarbruc ¨ ken) accepted our invitations to present keynote lectures. Jim Bezdek received the Kamp´ede F´ eriet Award, granted every two years on the occasion of the IPMU conference, in view of his eminent research contributions to the handling of uncertainty in clustering, data analysis and pattern recognition.

TheInternationalConferenceonInformationProcessingandManagementof- certaintyinKnowledge-BasedSystems,IPMU,isorganizedeverytwoyearswith the aim of bringing together scientists working on methods for the management of uncertainty and aggregationof information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas between th theoreticiansandpractitionersworkingintheseareasandrelated?elds. The13 IPMU conference took place in Dortmund, Germany, June 28-July 2, 2010. This volume contains 77 papers selected through a rigorous reviewing p- cess. The contributions re?ectthe richness ofresearchontopics within the scope of the conference and represent several important developments, speci?cally - cused on applications of methods for information processing and management of uncertainty in knowledge-based systems. We were delighted that Melanie Mitchell (Portland State University, USA), NihkilR. Pal(IndianStatisticalInstitute),BernhardSch. olkopf(MaxPlanckI- titute for Biological Cybernetics, Tubing .. en, Germany) and Wolfgang Wahlster (German Research Center for Arti?cial Intelligence, Saarbruc .. ken) accepted our invitationstopresentkeynotelectures.JimBezdekreceivedtheKamp'edeF' eriet Award,grantedeverytwo yearsonthe occasionofthe IPMUconference,in view ofhiseminentresearchcontributionstothehandlingofuncertaintyinclustering, data analysis and pattern recognition. Organizing a conference like this one is not possible without the assistance and continuous support of many people and institutions. We are particularly grateful to the organizers of sessions on dedicated topics that took place during the conference-these 'special sessions' have always been a characteristic e- ment of the IPMU conference. Frank Klawonn and Thomas Runkler helped a lottoevaluateandselectspecialsessionproposals. Thespecialsessionorganizers themselves renderedimportant assistancein the reviewingprocess,that was f- thermore supported by the Area Chairs and regular members of the Programme Committee. ThomasFober wasthe backboneon severalorganizationaland el- tronic issues, and also helped with the preparation of the proceedings.

The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

This book constitutes the refereed proceedings of the 6th International Conference on Scalable Uncertainty Management, SUM 2012, held in Marburg, Germany, in September 2012. The 41 revised full papers and 13 revised short papers were carefully reviewed and selected from 75 submissions. The papers cover topics in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information including applications in decision support systems, machine learning, negotiation technologies, semantic web applications, search engines, ontology systems, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.

This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.

This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.

This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.