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Leveraging the learning power of examples in automated constraint acquisition. (English) Zbl 1152.68540
Wallace, Mark (ed.), Principles and practice of constraint programming – CP 2004. 10th international conference, CP 2004, Toronto, Canada, September 27–October 1, 2004. Proceedings. Berlin: Springer (ISBN 978-3-540-23241-4/pbk). Lecture Notes in Computer Science 3258, 123-137 (2004).
Summary: Constraint programming is rapidly becoming the technology of choice for modeling and solving complex combinatorial problems. However, users of constraint programming technology need significant expertise in order to model their problem appropriately. The lack of availability of such expertise can be a significant bottleneck to the broader uptake of constraint technology in the real world. In this paper we are concerned with automating the formulation of constraint satisfaction problems from examples of solutions and non-solutions. We combine techniques from the fields of machine learning and constraint programming. In particular we present a portfolio of approaches to exploiting the semantics of the constraints that we acquire to improve the efficiency of the acquisition process. We demonstrate how inference and search can be used to extract useful information that would otherwise be hidden in the set of examples from which we learn the target constraint satisfaction problem. We demonstrate the utility of the approaches in a case-study domain.
For the entire collection see [Zbl 1139.68008].

MSC:
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
68T05 Learning and adaptive systems in artificial intelligence
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