Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Continuous case-based reasoning
Artificial Intelligence
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
A framework for the management of past experiences with time-extended situations
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
When Experience Is Wrong: Examining CBR for Changing Tasks and Environments
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A Case-Based Approach for the Classification of Medical Time Series
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
SOFT-CBR: a self-optimizing fuzzy tool for case-based reasoning
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
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We focus on the problem of selecting the few vehicles in a fleet that are expected to last the longest without failure. The prediction of each vehicle's remaining life is based on the aggregation of estimates from ‘peer' units, i.e. units with similar design, maintenance, and utilization characteristics. Peers are analogous to neighbors in Case-Based Reasoning, except that the states of the peer units are constantly changing with time and usage. We use an evolutionary learning framework to update the similarity criteria for peer identification. Results indicate that learning from peers is a robust and promising approach for the usually data-poor domain of equipment prognostics. The results also highlight the need for model maintenance to keep such a reasoning system vital over time.