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.
These twenty-eight contributions report advances in one of the most active research areas in artificial intellgence. Qualitative modeling techniques are an essential part of building second generation knowledge-based systems. This book provides a timely overview of the field while also giving some indications about applications that appear to be feasible now or in the near future. Chapters are organized into sections covering modeling and simulation, ontologies, computational issues, and qualitative analysis.Modeling a physical system in order to simulate it or solve particular problems regarding the system is an important motivation of qualitative physics, involving formal procedures and concepts. The chapters in the section on modeling address the problem of how to set up and structure qualitative models, particularly for use in simulation. Ontology, or the science of being, is the basis for all modeling. Accordingly, chapters on ontologies discuss problems fundamental for finding representational formalism and inference mechanisms appropriate for different aspects of reasoning about physical systems.Computational issues arising from attempts to turn qualitative theories into practical software are then taken up. In addition to simulation and modeling, qualitative physics can be used to solve particular problems dealing with physical systems, and the concluding chapters present techniques for tasks ranging from the analysis of behavior to conceptual design.Boi Faltings is Associate Professor of Computer Science at the Swiss Federal Institute of Technology, Lausanne. Peter Struss is Head of the Advanced Reasoning Methods Group at Siemens Corporate Research and Development in Munich.
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.
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.
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