运用蓝宝石因果关系模型和扩展集成设计模型分析了设计中的推理模式

IF 1.7 3区 工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
A. Bhatt, A. Majumder, A. Chakrabarti
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引用次数: 3

摘要

摘要文献表明,人们通常通过归纳来理解知识,通过综合来产生知识。本文回顾了各种推理模式——解释性溯因法、创新性溯因法、演绎法和归纳法——这些都是早期研究人员提出的设计过程中重要的推理模式。首先,我们的论文扩展了早期关于溯因推理的工作,溯因推理是合成过程中涉及的一种基本推理模式,通过“蓝宝石”因果关系模型来理解溯因推理的作用。利用蓝宝石模型对设计中溯因推理的解释与现有模型的解释进行了比较。其次,利用“设计扩展集成模型”对设计综合过程中的各种推理模式进行捕捉和分析。对参与者言语和结果的分析表明,该模型能够解释设计中出现的各种推理模式。结果表明,上述模型可以提供更广泛的设计综合推理说明。早期对这两个模型的实证验证进一步支持了它们的解释能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing the modes of reasoning in design using the SAPPhIRE model of causality and the Extended Integrated Model of Designing
Abstract Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.
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来源期刊
CiteScore
4.40
自引率
14.30%
发文量
27
审稿时长
>12 weeks
期刊介绍: The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.
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