Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Visual information seeking: tight coupling of dynamic query filters with starfield displays
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Direct manipulation for comprehensible, predictable and controllable user interfaces
Proceedings of the 2nd international conference on Intelligent user interfaces
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
Dynamic Queries for Visual Information Seeking
IEEE Software
A Method of Distributed Problem Solving on the Web
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Designing example-critiquing interaction
Proceedings of the 9th international conference on Intelligent user interfaces
Proceedings of the 9th international conference on Intelligent user interfaces
Towards an intelligent mobile travel assistant
Proceedings of the 2004 ACM symposium on Applied computing
Advanced preference query processing for e-commerce
Proceedings of the 2008 ACM symposium on Applied computing
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Many information systems are used in a problem solving context. Examples are travel planning systems, catalogs in electronic commerce, or agenda planning systems. They can be made more useful by integrating problem-solving capabilities into the information systems. This poses the challenge of scalability: when hundreds of users access a server at the same time, it is important to avoid excessive computational load.In this paper, we present an approach, called reality, that allows to significantly extend the reach of electronic commerce in travel. Our application addresses in particular the challenge of modeling customers' personal preferences and providing solutions that are tailored to just those preferences. In contrast to existing technology, which allow to optimize only a small and predefined set of preferences, our tool allows a wide variety that can accurately model the preferences of different customers.