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Article Dans Une Revue Computers in Industry Année : 2018

Experience capitalization to support decision making in inventive problem solving

Résumé

The theory of inventive problem solving (TRIZ) is based on the use of tools and their related knowledge sources for solving different types of inventive problems. Using classical TRIZ tools to solve a specific problem requires additional knowledge such as the expert's accumulated know-how in their problem solving practice (i.e. experience). In order to facilitate the use of experience, this paper explores a new inventive problem solving approach based on experience capitalization. Accordingly, the user with less expertise can solve new problems by reusing or revising the old solutions of other people. This approach is based on the use of the case-based reasoning (CBR) for collecting and rapidly accessing the experiences. A case study illustrates the problem solving process based on the proposed approach. In addition to that, we conducted a set of experiments to evaluate our approach. We considered a number of new problems whose resolution is performed in three different ways: manually, using an existing rule-based approach and using our proposed approach. The evaluation demonstrates that our approach gives better results and improves decision making in the inventive design process.
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Dates et versions

hal-02047105 , version 1 (23-02-2019)

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Pei Zhang, Amira Essaid, Cecilia Zanni-Merk, Denis Cavallucci, Sarra Ghabri. Experience capitalization to support decision making in inventive problem solving. Computers in Industry, 2018, 101, pp.25--40. ⟨10.1016/j.compind.2018.06.001⟩. ⟨hal-02047105⟩
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