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The Competence of Sub-Optimal Theories of Structure Mapping on Hard Analogies

机译:硬类比下次优结构映射理论的胜任力

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Structure-mapping is a provably NP-Hard problem which is argued to lie at the core of the human metaphoric and analgoical reasoning faculties. This nP-Hardness has meant that early attempts at optimal solutions to the problem have had to be augmented with sub-optimal heuristics to ensure tractable performance. This paper considers various grounds for qualifying the competence of such heuristic approaches, and offers an evalaution of the sub-optimal performance of three different models of analogy, SME, ACME and Sapper.
机译:结构映射是一个可证明的NP-Hard问题,被认为是人类隐喻和合乎逻辑的推理能力的核心。这种nP-Hardness意味着必须使用次优的启发式方法来增强对问题的最佳解决方案的早期尝试,以确保易于处理的性能。本文考虑了使这种启发式方法具有资格的各种理由,并对三种不同的类比模型(SME,ACME和Sapper)的次优性能进行了评估。

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