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Multi-modal diagnosis combining case-based and model-based reasoning: a formal and experimental analysis

机译：结合基于案例和基于模型的推理的多模式诊断：形式和实验分析

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Integrating different reasoning modes in the construction of an intelligent system is one of the most interesting and challenging aspects of modern AI. Exploiting the complementarity and the synergy of different approaches is one of the main motivations that led several researchers to investigate the possibilities of building multi-modal reasoning systems, where different reasoning modalities and different knowledge representation formalisms are integrated and combined. Case-Based Reasoning (CBR) is often considered a fundamental modality in several multi-modal reasoning systems; CBR integration has been shown very useful and practical in several domains and tasks. The right way of devising a CBR integration is however very complex and a principled way of combining different modalities is needed to gain the maximum effectiveness and efficiency for a particular task. In this paper we present results (both theoretical and experimental) concerning architectures integrating CBR and Model-Based Reasoning (MBR) in the context of diagnostic problem solving. We first show that both the MBR and CBR approaches to diagnosis may suffer from computational intractability, and therefore a careful combination of the two approaches may be useful to reduce the computational cost in the average case. The most important contribution of the paper is the analysis of the different facets that may influence the entire performance of a multi-modal reasoning system, namely computational complexity, system competence in problem solving and the quality of the sets of produced solutions. We show that an opportunistic and flexible architecture able to estimate the right cooperation among modalities can exhibit a satisfactory behavior with respect to every performance aspect. An analysis of different ways of integrating CBR is performed both at the experimental and at the analytical level. On the analytical side, a cost model and a competence model able to analyze a multi-modal architecture through the analysis of its individual components are introduced and discussed. On the experimental side, a very detailed set of experiments has been carried out, showing that a flexible and opportunistic integration can provide significant advantages in the use of a multi-modal architecture.
机译：在智能系统的构建中集成不同的推理模式是现代AI最有趣和最具挑战性的方面之一。利用不同方法的互补性和协同作用是促使几位研究人员研究构建多模式推理系统的可能性的主要动机之一，其中将不同的推理模式和不同的知识表示形式主义进行了整合和组合。基于案例的推理（CBR）在几种多模式推理系统中通常被认为是一种基本模式。在几个领域和任务中，CBR集成已被证明非常有用和实用。但是，设计CBR集成的正确方法非常复杂，并且需要一种组合不同模式的原则方法来获得特定任务的最大效力和效率。在本文中，我们介绍了在诊断问题解决的背景下，将CBR与基于模型的推理（MBR）集成在一起的体系结构的结果（理论和实验）。我们首先表明，MBR和CBR的诊断方法都可能遭受计算难点的困扰，因此，两种方法的仔细组合对于减少平均情况下的计算成本可能很有用。本文最重要的贡献是分析了可能影响多模式推理系统整体性能的不同方面，即计算复杂性，系统解决问题的能力以及所产生的解决方案集的质量。我们表明，能够估计各种模式之间正确协作的机会主义和灵活架构可以针对每个性能方面表现出令人满意的行为。在实验和分析水平上都进行了不同的CBR集成方法的分析。在分析方面，介绍并讨论了能够通过分析单个组件的分析来分析多模式体系结构的成本模型和能力模型。在实验方面，已经进行了非常详细的一组实验，表明灵活和机会主义的集成可以在使用多模式体系结构中提供显着的优势。

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