A Toolkit for Constraint-Based Inference Engines

Solutions to combinatorialsearch problems can beneet from custom-made constraint-based inference engines that go beyond depth-rst search. The concurrent constraint language Oz provides support for programming inference engines. The Mozart system for Oz comes with several engines, extended in dimensions such as interaction, visualiza-tion, and optimization. However, these 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. The Search-ToolKit, a public domain search library for Oz based on the presented architecture, provides evidence for the practicality of the design.