Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Constraint Propagation and Value Acquisition: Why we should do it Interactively
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Constraint Processing
Autonomous Agents and Multi-Agent Systems
AI Communications - Constraint Programming for Planning and Scheduling
Complexity of terminating preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Computing possible and necessary winners from incomplete partially-ordered preferences
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Uncertainty in preference elicitation and aggregation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Winner determination in sequential majority voting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incompleteness and incomparability in preference aggregation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Dealing with incomplete preferences in soft constraint problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A cost-based model and algorithms for interleaving solving and elicitation of CSPs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Active learning of combinatorial features for interactive optimization
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Generating diverse plans to handle unknown and partially known user preferences
Artificial Intelligence
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We consider soft constraint problems where some of the preferences may be unspecified. This models, for example, settings where agents are distributed and have privacy issues, or where there is an ongoing preference elicitation process. In this context, we study how to find an optimal solution without having to wait for all the preferences. In particular, we define algorithms, that interleave search and preference elicitation, to find a solution which is necessarily optimal, that is, optimal no matter what the missing data will be, with the aim to ask the user to reveal as few preferences as possible. We define a combined solving and preference elicitation scheme with a large number of different instantiations, each corresponding to a concrete algorithm, which we compare experimentally. We compute both the number of elicited preferences and the user effort, which may be larger, as it contains all the preference values the user has to compute to be able to respond to the elicitation requests. While the number of elicited preferences is important when the concern is to communicate as little information as possible, the user effort measures also the hidden work the user has to do to be able to communicate the elicited preferences. Our experimental results on classical, fuzzy, weighted and temporal incomplete CSPs show that some of our algorithms are very good at finding a necessarily optimal solution while asking the user for only a very small fraction of the missing preferences. The user effort is also very small for the best algorithms.