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Binary CSPs and Heuristics for Modeling and Diagnosing Dynamic Systems

机译:二元CSP和启发式用于建模和诊断动态系统

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In this paper we concentrate on practicalaspects of qualitative modeling and reasoning about physical systems, reporting our experience within the VMBD project1 in applying Constraint Programming techniques to the task of diagnosing a real-life automotive subsystem. We propose a layered modeling approach: qualitative deviations equations as a high level model description language, and Constraint Satisfaction Problems (CSPs)with non binary constraints as underlying implementation formalism. An implementation of qualitative equations systems based on non binary constraints is presented, discussing the applicability o various heuristics. In particular, a greedy heuristic algorithm for cycle cutset decomposition and variable ordering is proposed for efficient reasoning on CSPs derived from qualitative equations. A prototype implementation of a constraint-based diagnostic engine has been developed using CLP(FD) and C++, and some preliminary results on the proposed modeling approach and heuristics are reported.
机译:在本文中,我们专注于对物理系统的定性建模和推理的实用普及,在将约束规划技术应用于诊断现实生活汽车子系统的任务时,报告我们在VMBD项目中的经验。我们提出了一个分层建模方法:定性偏差方程作为高级模型描述语言,以及具有非二元限制的约束满足问题(CSP),作为基础实施的形式主义。介绍了基于非二元约束的定性方程系统的实现,讨论了各种启发式的适用性。特别地,提出了一种贪婪的启发式算法,用于源自定性方程的CSP上的有效推理。已经使用CLP(FD)和C ++开发了基于约束的诊断发动机的原型实现,并报告了提出的建模方法和启发式的一些初步结果。

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