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arXiv 1302.4986cs.AI
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概率模型诊断中利用系统层次计算维修计划

Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-based Diagnosis

Stanford University(斯坦福大学);Microsoft Research(微软研究院)

Sampath Srinivas, Eric J. Horvitz

摘要:

基于模型的诊断的目标是隔离异常系统行为的原因,并推荐廉价的修复措施作为响应。在一般情况下,预先计算最优修复策略是棘手的。到目前为止,解决这个问题的调查人员已经探索了近似,要么对系统模型施加限制(如单一故障假设),要么计算有限的前瞻性的即时最佳行动。在本文中,我们开发了一个公式的修复模型为基础的诊断和修复算法,计算最佳的行动序列。这种最优方法是昂贵的,但可以应用于预先计算的最优修复策略紧凑的系统。我们将展示如何利用分层系统规范,使这种方法易于处理的大型系统。在引入层次结构时,我们还考虑了简单地替换组件和分解组件以修复其子组件之间的权衡。分层修复算法适用于最优修复策略的离线预计算。该算法的修改利用迭代深化方案来权衡推理时间和计算策略的质量。

英文摘要:

The goal of model-based diagnosis is to isolate causes of anomalous system behavior and recommend inexpensive repair actions in response. In general, precomputing optimal repair policies is intractable. To date, investigators addressing this problem have explored approximations that either impose restrictions on the system model (such as a single fault assumption) or compute an immediate best action with limited lookahead. In this paper, we develop a formulation of repair in model-based diagnosis and a repair algorithm that computes optimal sequences of actions. This optimal approach is costly but can be applied to precompute an optimal repair strategy for compact systems. We show how we can exploit a hierarchical system specification to make this approach tractable for large systems. When introducing hierarchy, we also consider the tradeoff between simply replacing a component and decomposing it to repair its subcomponents. The hierarchical repair algorithm is suitable for off-line precomputation of an optimal repair strategy. A modification of the algorithm takes advantage of an iterative deepening scheme to trade off inference time and the quality of the computed strategy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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