Constraint programming is a very successful ?fth-generation software techn- ogy with a wide range of applications. It has attracted a large community of researchers that is particularly strong in Europe. In particular, constraint programming is the focus of the Working Group on Constraints of the European Research Consortium for Informatics and Mat- matics (ERCIM) as well as a major interest of the European Network on C- putational Logic (CoLogNET). These groups jointly sponsored a workshop on Constraint Satisfaction and Constraint Logic Programming (CSCLP 2004) held June23-25attheEcolePolytechniqueF ed eraledeLausanne(EPFL)inSwitz- land. It was hosted by the Arti?cial Intelligence Laboratory of the EPFL, which is also a member of both groups. Thisbookpresentsacollectionofpapersthatareeitherrevisedandextended versions of papers accepted at the workshop, or were submitted in response to theopencallforpapersthatfollowed.The15papersinthisvolumewereselected from 30 submissions by rigorous peer review. The editors would like to take the opportunity to thank all authors and reviewers for the hard work they contributed to producing this volume. We also thank ERCIM and CoLogNET for their support of the workshop and the ?eld of constraint programming in general. We hope the reader will ?nd this volume helpful for advancing their understanding of issues in constraint programming. December 2004 Boi Faltings Adrian Petcu Francois, Fages Francesca Rossi Organization This workshopwas jointly organized as the 9th Meeting of the ERCIM Working GrouponConstraints, coordinatedbyFrancois, Fages, andthe2ndAnnualWo- shop of the CoLogNET area on Constraint Logic Programming, coordinated by Francesca Rossi. OrganizingInstitutes TheorganizationwashandledbytheEPFL, INRIAandtheUniversityofPadua."

This book constitutes the refereed proceedings of the Third European Workshop on Case-Based Reasoning, EWCBR-96, held in Lausanne, Switzerland, in November 1996.
Case-based reasoning is an appealing technique for dealing with the knowledge acquisition bottleneck in computer applications; solutions to new problems are found by adapting similar experience from the past, called cases. The 38 revised full papers presented were carefully selected from a broad variety of submissions after a thorough refereeing process. The volume refleats the state of the art in case-based reasoning research and applications.

Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.