Structural-model approach of causal reasoning in problem solving processes

Taking into account the experience feedback on complex problems solving in industrial organizations is a way to improve the quality of products and processes. However, few academic works deal with representation and instrumentation of experience feedback systems. We propose in this paper a model of experiences and mechanisms to use these experiences. More specifically, we wish to promote the reuse of expert analysis already performed to propose an a priori analysis to address a new problem. The proposed approach is based on a representation of the context of the experience using conceptual markers and a conceptual representation of the analysis explicitly incorporating expert opinions and their combination.

[1]  Christiane Fellbaum,et al.  Combining Local Context and Wordnet Similarity for Word Sense Identification , 1998 .

[2]  Philippe Smets,et al.  Decision making in the TBM: the necessity of the pignistic transformation , 2005, Int. J. Approx. Reason..

[3]  Rosina O. Weber,et al.  Intelligent lessons learned systems , 2001, Expert Syst. Appl..

[4]  Alessandro Saffiotti,et al.  The Transferable Belief Model , 1991, ECSQARU.

[5]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[6]  Minh Ha-Duong.  Hierarchical fusion of expert opinions in the Transferable Belief Model, application to climate sensitivity , 2008, Int. J. Approx. Reason..

[7]  D. Kolb.  Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[8]  Céline Tea.  Retour d'expérience et données subjectives : quel système d'information pour la gestion des risques ? , 2009 .

[9]  Hisham Al-Mubaid,et al.  Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Laurent Geneste,et al.  Elaboration and Exploitation of Lessons Learned , 2002, Intelligent Information Processing.

[11]  Minkoo Kim,et al.  Topic distillation using hierarchy concept tree , 2003, SIGIR '03.

[12]  David Sánchez,et al.  An ontology-based measure to compute semantic similarity in biomedicine , 2011, J. Biomed. Informatics.

[13]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.