NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Comparison-Based Recommendation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Highlighting Hard Patterns via AdaBoost Weights Evolution
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
An Efficient Boosting Algorithm for Combining Preferences
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Toward case-based preference elicitation: similarity measures on preference structures
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Collaborative case-based preference elicitation
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
A CBR-Based Approach for Ship Collision Avoidance
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Case Learning in CBR-Based Agent Systems for Ship Collision Avoidance
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Case learning for CBR-based collision avoidance systems
Applied Intelligence
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Very often a planning problem can be formulated as a ranking problem: i.e. to find an order relation over a set of alternatives. The ranking of a finite set of alternatives can be designed as a preference elicitation problem. While the case-based preference elicitation approach is more effective with respect to the first principle methods, still the scaling problem remains an open issue because the elicitation effort has a quadratic relation with the number of alternative cases. In this paper we propose a solution based on the machine learning techniques. We illustrate how a boosting algorithm can effectively estimate pairwise preferences and reduce the effort of the elicitation process. Experimental results, both on artificial data and a realworld problem in the domain of civil defence, showed that a good trade-off can be achieved between the accuracy of the estimated preferences, and the elicitation effort of the end user.