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Computer-Aided Problem Solving - Part 2: A Dialogue-Based System to Support the Analysis of Inventive Problems

The paper illustrates an original model and a dialogue-based software application that have been developed by integrating the logic of ARIZ with some OTSM-TRIZ models, in order to guide a user, also with no TRIZ background, to the analysis of
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  D. Cavallucci, R. De Guio, and G. Cascini (Eds.): CAI 2011, IFIP AICT 355, pp. 132–148, 2011. © Springer-Verlag Berlin Heidelberg 2011 Computer-Aided Problem Solving - Part 2: A Dialogue-Based System to Support the Analysis of Inventive Problems Niccolò Becattini 1 , Yuri Borgianni 2 , Gaetano Cascini 1 , and Federico Rotini 2   1  Politecnico di Milano, Dip. di Meccanica, Via Giuseppe la Masa, 1, 20156 Milan, Italy 2  Università di Firenze, Dip. di Mecc. e Tecn. Ind., Via Santa Marta, 3, 50139 Florence, Italy {Yuri.Borgianni,Federico.Rotini} Abstract.  The paper illustrates an srcinal model and a dialogue-based software application that have been developed by integrating the logic of ARIZ with some OTSM-TRIZ models, in order to guide a user, also with no TRIZ background, to the analysis of inventive problems. The dialogue-based procedure brings to the construction of a model of the inventive problem, which is used both to trigger new solutions by highlighting different solving perspectives and to start an automatic knowledge search within technical and scientific information. The prototype system has been tested with students at Politecnico di Milano and at the University of Florence. The paper details the structure of the algorithm and the results of the first validation activity. Keywords:  Computer-Aided Innovation, problem solving, OTSM-TRIZ, conceptual design, dialogue-based system. 1 Introduction Despite TRIZ is recognized among its expert practitioners as a theory which efficiently improves individuals and organizations problem solving capabilities, its wide dissemination is still dramatically limited by the learning efforts required to master its tools and to assimilate its srcinal thinking logic. Among the main causes which hinder its diffusion in the industrial world, a relevant role is certainly played by the investment needed to introduce TRIZ logic and tools in the existing product development cycle. Several organizational and educational models have been proposed so far, as also in [1], but still several critical open issues remain: •   The percentage of people who starts adopting some TRIZ instruments after attending an introductory course is very limited. •   “Simplified TRIZ”, too often intended as a fuzzy application of the contradiction matrix and the inventive principles, is closer to a brainstorming session with guided “stimuli” rather than to the TRIZ problem solving process, and indeed its potential is much more limited. Thus, a conflict exists between a proper assimilation of a TRIZ “way of thinking” and the time available in the industrial reality to learn and practice.   A Dialogue-Based System to Support the Analysis of Inventive Problems 133 •   Such a conflict is even tougher for SMEs, since in a small organization each employee covers several roles and it is quite impossible to dedicate sufficient time and efforts to TRIZ learning, while keeping the other functions. •   Several TRIZ-based software applications have been proposed in the market since the ‘90s, but these systems are not useful to speed up the TRIZ learning process and are marginally usable by people without any TRIZ background. Indeed, many companies, that in the past acquired some licenses of these applications without a proper TRIZ education, largely contribute to the promotion of a bad image of TRIZ. Especially in the last decade, the literature witnesses the need to create TRIZ-based tools addressed to help non-practitioners, with the double purpose of offering intuitive ready-to-use problem solving skills and providing a learning approach to fully take advantage of TRIZ capabilities. In such perspective Dubois et al. [2] propose an algorithmic framework to support the representation of contradictions and standard solutions. In the same context, the authors decided to start a research activity aimed at defining a new role for TRIZ-based computer applications, i.e. CAI problem-solving “coaches” for non-trained users. According to the authors’ intention, being guided by a computer application, a designer with no TRIZ background should be able to improve his problem solving capability since the first usage of the software and at the same time to acquire the ARIZ logic through a learn-by-doing process. The overall motivation of the research is further detailed in [3], where the general requirements of a computerized system for problem solving are identified through a detailed discussion about typical classifications of engineering problems, approaches to problem solving and related computer implementations, the advantages of a dialogue-based human-machine interaction to elicit his knowledge and stimulate his creative skills. On the basis of the insights arising from this survey, the authors hereby propose an srcinal dialogue-based system, founded on TRIZ logic, and suitable for software implementation. According to the conclusions drawn in [3], here assumed as a starting point for the development of the computer-implementable algorithm, in order to support inventive problem solving activities within the conceptual design stage, CAI systems should overcome the dichotomy between cognitive and systematic problem solving models, both to exploit user’s knowledge and to guide the design activity towards the solution with a step by step process. The system should also allow an abstract description of the problem in order to enlarge the solutions space and to link the design process with relevant external sources of information from different expertise domains. Eventually, the adoption of a natural language dialogue-based interaction with the user can be considered as an effective means to support designers with no specific background on problem solving methodologies, but also to improve their capabilities through a learning-by-doing process. The paper hence starts with a survey of the conceptual models adopted as a reference to build the computer-implementable problem solving algorithm. Then the structure of the algorithm is detailed, with a careful description of all its modules and an exemplary excerpt from one of them. The second part of the paper describes the experimental activity run with the MS degree and PhD students in mechanical  134 N. Becattini et al. engineering at Politecnico di Milano and University of Florence; the following discussion allows to point out some positive conclusions about the potentialities of the proposed algorithm and also to identify further directions of investigation. 2 Development of a System to Support the Analysis of Inventive Problems The double goal of fully exploiting TRIZ capabilities and envisaging both cognitive and systematic aspects of problem solving methods, as a need emerged by the review reported in [3], has been the basis for the selection of the theoretical pillars and the models to build a Computer-Aided problem solving framework. Moreover, the mentioned survey has addressed the authors towards the development of a dialogue-based system for assisting the inventive tasks of the conceptual design phase. This section briefly mentions the theoretical reference items and details the srcinal dialogue-based algorithm developed by the authors as the foundation for the software problem solving application. 2.1 OTSM-TRIZ Models as a Meta-Cognition Framework for Inventive Problem Solving As discussed in [3], a synthesis beyond the dichotomy between cognitive and systematic approaches to problem solving allows to avoid trial and error, build efficient procedures, leverage the available knowledge resources of individuals and teams and highlight knowledge lacks to be covered with new information sources. According to the authors’ experience, the most comprehensive and organic suite of models describing a problem solving process with the abovementioned characteristics is provided by OTSM-TRIZ [4] and includes: •   Hill model (abstraction-synthesis); •   Tongs model (from current situation to ideality, barriers identification); •   Funnel model (convergent process); •   System Operator (system thinking). These models should not be considered as alternative paths for transforming a problematic situation into a solution, but as complementary descriptions of the characteristics of an efficient problem solving process. As recalled in the Introduction, TRIZ has been proved to be a very useful help to designers for developing innovative products. Its set of tools and concepts allows to provide reliable results when addressing non-typical problems. However, substantial limitations arise due to the considerable learning efforts required to master its logic and tools, as for example witnessed by well known industrial players within the TRIZ community [5, 6]. For this reason, a specific goal of the present research is to allow even users without a strong vocational experience to achieve viable conceptual solutions. Moreover, due to the given market boundaries, the recourse to time-consuming and potentially expensive specialization courses has to be excluded. This issue is especially relevant for SMEs, for which a considerable growth in the need to employ   A Dialogue-Based System to Support the Analysis of Inventive Problems 135 systems and software for innovation is expected [7], that therefore could be considered the primary users of the tool under development. Since the innovation system has to be addressed mainly to inexperienced practitioners, particular attention has to be paid, beyond the foolproof use, towards the removal of TRIZ specific terminology. Thus the application has to foresee TRIZ models, but the user/system interface has to be built through a common language, using at the greatest extent terms and concepts introduced by the designer himself/herself. 2.2 Description of the Algorithm: Logical Blocks and Further Outputs The srcinal contribution of this paper is constituted by an algorithm for problem analysis, structured in the form of a dynamic dialogue, suitable for implementation in a software application. The underpinning logic of OTSM-TRIZ and several classical TRIZ tools are integrated in order to widely describe the topic of the investigation and to remark the most relevant issues to be considered for the problem solving activity and, if necessary, for the knowledge search. The dialogue based system helps at first the user in exploiting his know how by suggesting problem solving paths, that don’t require external expertise to be implemented. Thanks to the investigation of the parameters affecting the undesired issues arising in the system, the designer can individuate factors to be modified in order to reformulate the problem as a typical case. Moreover, among the outcomes of the innovation system, the algorithm provides indications for suitable problem solving alternatives through different TRIZ tools, e.g. separating in time/space, trimming useless or low-valued components, opportunities to turn the undesired effect into a useful output, re-thinking the ways to perform the main function or to deliver the same benefits. However, as already summarized in the introduction, the formalization of the problem should also aid the knowledge search, being the information retrieval within the expertise domain preferable to that in external fields, due to an easier and quicker implementation of the generated ideas. In any case, the knowledge search outside the expertise domain should be accomplished just when the abstraction process has been completely and successfully performed, i.e., when a so called physical contradiction has been correctly formulated. In order to fulfil the requirements and to cover all the options for problem solving and knowledge search, the framework of the algorithm includes a set of logical blocks aimed at examining different aspects of the system to be designed and/or improved. The network of links among the blocks and the single nodes of the algorithm determine an extensive bundle of paths and cycles to refine the problem formulation, depicted in Fig. 1. With the objective of easing the usability of the system and the formalization of the problem, the following measures have been taken: •   the nodes of the algorithm are either open questions, choices or messages, intended to provide proper hints in performing the problem solving process; •   the questionnaire employs a common terminology, avoiding TRIZ jargon; •   the text of questions and suggestions resorts to previously introduced terms and items;  136 N. Becattini et al. Fig. 1.  Network of logical blocks and outputs of the questioning procedure •   some answer examples are provided, as well as their grammatical form, in order to clarify the purpose of the open questions and to provide a more sound text in the downstream nodes of the algorithm; •   the questioning procedure is rich of checks in order to verify the correctness of the user’s inputs and to provide a feedback to the user about the outcome of the dialogue. With the objective of addressing the user towards the most proper description of the situation, the algorithm performs a preliminary distinction among tasks concerning the presence of negative effects or drawbacks, the required implementation of new useful functions and the enhancement of systems with relevant under-performances. The individuation of an undesired effect should lead to the investigation of the features and the phenomena that provoke it and, subsequently, to the formalization of a physical contradiction, grounded on a control parameter leading to mismatching outputs according to the value it assumes. However, even when the user doesn’t address the formulation of the problem through the task concerning the elimination of a harmful output, several attempts are carried out with the aim of redefining the model of the system under investigation. The objective is to lead the user towards the formalization of the negative effect responsible for the unsatisfactory or missing functions and, eventually, towards the identification of a contradiction. The most straightforward path for formulating the contradiction,
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