Abstract
Solutions to combinatorial search problems can benefit from custom-made constraint-based inference engines that go beyond depth-first search. Several constraint programming systems support the programming of such inference engines through programming abstractions. For example, the Mozart system for Oz comes with several engines, extended in dimensions such as interaction, visualization, and optimization. However, so far such extensions are monolithic in their software design, not catering for systematic reuse of components.
We present an object-oriented modular architecture for building inference engines that achieves high reusability and supports rapid prototyping of search algorithms and their extensions. For the sake of clarity, we present the architecture in the setting of a C++ constraint programming library. The SearchToolKit, a search library for Oz based on the presented architecture, provides evidence for the practicality of the design.
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Yong Chew, T., Henz, M., Boon Ng, K. (1999). A Toolkit for Constraint-Based Inference Engines. In: Pontelli, E., Santos Costa, V. (eds) Practical Aspects of Declarative Languages. PADL 2000. Lecture Notes in Computer Science, vol 1753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46584-7_13
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DOI: https://doi.org/10.1007/3-540-46584-7_13
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