Literature review on the creativity of CBR applications - Springer Link

Artif Intell Rev (2013) 40:379–390 DOI 10.1007/s10462-011-9286-y

Literature review on the creativity of CBR applications Zhiqiang Zhu · Hung-Yao Hsu · Sev Nagalingam · Li Geng

Published online: 13 November 2011 © Springer Science+Business Media B.V. 2011

Abstract CBR solves new problems by reusing the old solutions in similar context. As an effective problem-solving method, CBR has been widely applied in various areas. However, the question of whether CBR normally generates creative solutions has not been answered properly according to recent research and thus deserves attention. Consequently, an extensive literature review on CBR applications has been provided in this paper. The results of this literature review are expected to emphasise the importance of creativity in CBR. Keywords

Case based reasoning · CBR · Creativity

1 Introduction 1.1 Case based reasoning CBR solves new problems by reapplying and adapting solutions that were used to solve old similar problems. A typical CBR process is comprised of five-Re steps (Aamodt and Plaza 1994) as illustrated in Fig. 1: (1) Retrieve: given a new problem situation, retrieve a set of stored cases whose problem descriptions are judged as the most similar to the new context; (2) Reuse: apply one or more solutions from these retrieved cases to the new situation, perhaps by combining them with each other or joining with other knowledge source; (3) Revise: use the retrieved solution as a starting point, and subsequently adapt it, as needed, in an attempt to solve the new problem; (4) Review: evaluate the outcome(s) when applying the constructed solution to the current problem. If the outcome is not acceptable, then the solution will require further revision; and Z. Zhu (B) · H.-Y. Hsu · S. Nagalingam · L. Geng School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Mawson Lakes, 5095 SA, Australia e-mail: [email protected]

123

380

Z. Zhu et al.

Input

New Problem

1. Retrieve

Case Library Retrieved Cases

5. Retain

Domain Model

2. Reuse

Outcome Retrieved Solution(s) 4. Review

3. Revise Revised Solution

Fig. 1 A view of the case-based reasoning problem-solving cycle (Aha 1998)

(5) Retain: after having past the evaluation, the new achieved solution can be added to the library as a new case. According to the diagram in Fig. 1, CBR takes a new problem as the input. On the basis of problem descriptions, CBR searches in its memory for the most similar prior case. Once having been retrieved, the previous case is reused or revised if necessary to suit the new situation. Then, the revised solution is reviewed on the basis of the new situation. Finally, the successful outcome is retained in the CBR memory for future reference. Consequently, as a problem solving method, CBR has been deployed in a wide variety of applications, such as planning (Seo et al. 2007; Lei et al. 2001), classification (Li and Yeh 2004; Ahn et al. 2007), diagnosis (Huang et al. 2007; Garrelli Guiu et al. 1999), decision supporting system (Frize and Walker 2000; Zhuang et al.) and others (Ross et al. 2002; Termsinsuwan et al. 1996; Thomasson et al. 2006; Tsai and Chiu 2007; Tseng et al. 2005; Vollrath et al. 1998; Vong et al. 2002; Praehofer and Kerschbaummayr 1999; Pulkkinen et al. 2008). 1.2 Creativity Creativity can be a fairly elusive concept (Rouse 1986; Bohm 2003; Runco 2007; Weisberg 2006; Pope 2005; Piirto 2004; Boden 2004). Plucker et al. (2004) proposed a synthesised definition of creativity after having analysed on 90 selected articles in creativity. In their definition (Plucker et al. 2004), creativity is the interaction among aptitude, process, and environment by which an individual or group produces a perceptible product that is both novel and useful as defined with a social context. From this definition, two recognised attributes of creativity are novelty and usefulness, which are also commonly implicated in literature (Goel and Singh 1998; Kryssanov et al. 1999, 2001; Runco 2007; Pope 2005). Creativity is highly valued in human society throughout history. The history of civilisation is regarded as a history of humankind’s creation and creativity in languages provide the foundation to human civilisation. Grand inventions from ancient to recent time such as techniques of paper making, printing, powder, steam engine, electric light, automobiles, TV, telephone, Internet, and the like have advanced our human society and brought us convenience.

123

Creativity of CBR applications

381

Creativity presently has a much wider purview than it once did in history (Kaufman and Baer 2005). It plays important roles and becomes a term appearing in many disciplines, such as industry, business, trade and commerce, education, science, arts, engineering, international relations. In addition to these traditional disciplines on the benefits of creativity, recent research revealed link between creativity and social welfare (Hoffmann et al. 2005), personal health, vocational and life success, reducing youth violence and others (Plucker et al. 2004). To explicitly demonstrate the popularity of this term, searches with ‘creativity’ as the key word were conducted in several leading searching engines and database in the middle of October 2011. Consequently, this term appeared about 171,000,000 times in the searching results by Google and 158,000,000 by Yahoo . SciencDirect , one of renowned scientific databases, showed 48,578 references of creativity to titles of journal articles, conference proceedings, and other research publications. Amazon.com listed 11,394 books with the word creativity in their titles. 1.3 Creativity of CBR When associate creativity with CBR, an interesting question can be raised, which is how is the creativity of CBR? In other words, does CBR normally generate creative solutions? Given the fact that creativity is treasured by our society, whether generating creative solutions is crucial to CBR, which serves as an effective problem-solving method. Researches with respect to the creativity of CBR had been done in the past. Leake (1996) suggested that creativity in CBR can be achieved by a flexible retrieval process which leads to a novel starting point for a new problem solving, a new correspondence after mapping, and a flexible case adaptation. Wills and Kolodner (1996) suggested that CBR systems need to allow flexible interleaving and communications among their internal processes for the evolvement of creativity. They believed that CBR can support creative problem solving by emphasising on four processes: problem re-description, evaluation, assimilation, and strategic control. In a review paper, Goel and Craw (2006) summarised the CBR innovation in design since 1990s, which included the systems of FAMING (Faltings and Sun 1996) and IDEAL (Bhatta and Goel 1997, 1998), and a new approach for case adaptation (Gómez de Silva Garza and Maher 1999). Other researches in the creativity of CBR can also be found in literature (Mcgovern et al. 1994; Jo et al. 1997; Pajula et al. 2001; Seuranen et al. 2005; Leake 1992; Navinchandra 1988; Stroulia and Geol 1992). However, all these research work aforementioned is essentially related to ‘how to make CBR creative?’ or ‘can CBR be creative?’ rather than reveals if CBR normally generates creative solutions. ‘Can do something’ is not absolutely equivalent in meaning to ‘what it normally does’. For example, nowadays human can go to Moon does not mean people normally transport between Earth to Moon for the time being. To answer this question, we continue to review the CBR method in terms of its definition and principles. The definition of CBR technique can be implied in some widely accepted descriptions. For example, in the view of Riesbeck and Schank (1989), a case-based system solves new problems by adapting solutions that were used to solve old problems. Leake (1996) stressed the heart of CBR is remembering and reusing specific experiences and the lessons that CBR provides. Watson (2003) defined CBR approach as the process of remembering an old plan, reusing it, and perhaps adapting a small part of it. All the above CBR descriptions emphasise that the core of the CBR method is reusing solutions in old context to cope with new problems, but none suggests if the new solutions should be creative. The fundamental principles of CBR are based on two tenets on the nature of the world (Kolodner 1993):

123

382

Z. Zhu et al.

(1) The world is regular and thus similar problems have similar solutions. Consequently, solutions for similar prior problems can be a useful starting point for the new problemsolving exercise. (2) Engineers tend to encounter recurring problems. Consequently, future problems are likely to be similar to the current or past problems. When the two principles hold, it is worthwhile to remember and reuse prior solutions in reasoning. However, these principles only indicates that the previous problem solutions are useful as a starting point in solving similar problems in the future, without considering the ending point. The problem solving process can be concluded with two possibilities: • One possibility is to search locally on the basis of the similar previous problem and then, if necessary, to adjust minimum its solution to suit the new problem. In some particular instances, when current situation is sufficiently similar as the one in the past, the solution for the past problem can be directly copied and applied to solve the current one without necessarily any modifications. • The other destination is to seek progressively the new and novel solutions for the new problems even similar precedents exist, which may be used as the starting point. In this regard, these two CBR principles have not explicitly revealed whether CBR normally generates creative solutions either. As no answer has been found to the question about the creativity of CBR through the examination of its definition and principles, we turn to investigate its applications in real practices. As discussed in Sect. 1.1, the CBR method has various applications. By studying the reasoning process in its applications can eventually suggest if CBR is normally adopted to produce creative solutions. For this purpose, a literature review on the creativity of CBR applications is conducted in Sect. 2. On the basis of the result of the literature review, a discussion about the creativity of CBR is provided in Sect. 3. Finally, this paper is summarised with conclusion and recommendations on future developments.

2 Literature review on the creativity of CBR applications 2.1 Sources of the available CBR applications A large amount of CBR applications and systems have been developed since the early 1980s (Watson 1997). Exhaustively searching all the available CBR applications could be an overwhelming task and nearly impossible. Therefore, this literature review for available CBR applications has been undertaken through three sources. They are: • Source one: A book of Kolodner (1993), which described eighty-three CBR applications until 1993; • Source two: A book of Watson (1997), which listed 135 industrial applications of the CBR method until 1997; and • Source three: A literature searching in the database, Elsevier’s ScienceDirect , which returned 141 articles (with 143 CBR applications) until July, 2008. In the source three, the CBR applications searched and obtained through the database ScienceDirect are used. This research returns 233 CBR literatures which includes 141 articles in the topic of various CBR applications. By contrast, searching with same key words via the IEEE Explorer fetched about 50 CBR publications. In terms of the amounts and

123


383

adequate coverage in various fields, CBR applications retrieved from the ScienceDirect have been adopted as the source three. The validity of these sources for CBR applications is determined in two aspects: • Each sources consists indiscriminately of various CBR applications; and • Three sources cover the range from the early CBR systems till the latest CBR applications. 2.2 Four levels of measurement The available CBR applications are categorised into groups in terms of their creativity. For this purpose, four levels of measurement on the creativity of these CBR applications are gradually imposed. They are: Level 1: Level 2: Level 3: Level 4:

the CBR systems, which cannot provide new solutions; the CBR systems, which can provide adapted solutions; the CBR systems, which can provide creative solutions; and the creative CBR systems, which are initially developed for providing creative solutions.

CBR systems in level 1 do not produce new solutions, where old solutions are retrieved either to give human suggestions or be reused in the new situations without any adaptation. In level 2, CBR systems propose adapted solutions which are obtained with adjustments on the old solutions, i.e. a solution can be found just by changing the geometric parameters associated with a given structure. In level 3, CBR systems propose creative solutions through particular processes such as combination (Koestler 1975; Boden 2004; Ishikawa and Terano 1996; Poincaré 1982), generalisation (Yan 1998; Gael 1997), analogy (Gael 1997; Mayer 1989; Itkonen 2005; Vosniadou and Ortony 1989; Gomes et al. 2006), and others (Guilford 1950; Csikszentmihalyi 1996; Simonton 1999), which can promote creativity. In the level 4, systems are built as creative CBR applications, where the creativity of solutions has been emphasised as one of the main objectives since the early stage of development. It is noted that a slight difference exists between the measurements of level 3 and level 4. In level 3, although the CBR systems can propose creative solutions, being creative may not be given priority. By contrast, the systems in level 4 are designed intentionally to be creative CBR applications, where creativity has been emphasised. Additionally, due to the fact that the CBR system in the fourth level can produce creative solutions, it also meets the measurement of the third level. Therefore, the CBR systems classified into level 4 are also inclusive to level 3. 2.3 Data analysis and results The CBR applications that have been collected from the three sources are analysed individually in accordance to the four levels of measurement. 2.3.1 Source one: the CBR applications from Kolodner’s book A survey result of CBR applications was included in the appendix part of Kolodner’s book (1993). This survey result consisted of, in total, eighty-three CBR systems that had been developed until 1993. Each CBR application is described in terms of background information, research emphasis, function, operation, as well as the reasoning process. The reasoning process includes the indexing scheme, retrieval algorithm, matching process, and adaptation methods when available.

123

384

Z. Zhu et al.

Table 1 Results of source one Number of CBR systems

Percentage

1. CBR system not providing new solutions

53

63.86

2. CBR system providing adapted solutions

24

28.92

3. CBR system providing creative solutions

6

7.23

4. Creative CBR systems

1

1.20

Total numbers of CBR systems

83

Table 2 Results of source two Number of CBR systems

Percentage 94.81


128


7

5.19


Not available

−


Not available

−


135

With respect to the four levels of measurement, the description of each CBR application provides information to conclude whether the CBR system can produce either adapted solution or creative solution. Further, the research emphasis of each CBR application can give evidence to identify if a CBR system had been designed as a creative problem solving system. The result of the four levels of measurement is shown in Table 1.

2.3.2 Source two: the CBR industrial applications from Watson’s book In total, 135 types of CBR industrial applications were listed in Watson’s book (1997). Watson categorised industrial CBR applications into two main types: classification tasks, and synthesis tasks. Classification tasks concern with reusing the best match old solutions for the new problems. Watson (1997) indicated that most of existing commercial CBR applications undertook classifications tasks and support primarily case retrieval. Synthesis tasks can create solutions by the adaptation of previous solutions. Therefore, classification tasks correspond to the first level of measurement, whereas synthesis tasks match the second level of measurement. Since relevant information is not available, the capacity of CBR systems in creativity can not be measured and therefore the third and fourth levels of measurement are not examined in source two. The result of source two is shown in Table 2. 2.3.3 Source three: CBR applications through the ScienceDirect database In total, 233 CBR technical literatures including journal papers and conference papers are retrieved from the ScienceDirect publication database. Through online resources, a single phrase “case based reasoning” is used for searching articles that were published until July 2008. Amongst these retrieved 233 literatures, 141 articles presented, proposed and discussed 143 types of CBR applications and systems. The result of source three is shown in Table 3.

123


385

Table 3 Results of source three Number of CBR systems

Percentage


98

68.53


38

26.57


7

4.90


3

2.10


143

Fig. 2 Summary of the results from the three sources of CBR applications

2.4 Summary of results The results of these three CBR application sources have been collated as depicted in Fig. 2. The data results of the three sources show great consistency in all four levels of measurements, particularly between source one and source three. Results in source two is also in agreement with the other two sources in suggesting that most of CBR applications are not providing creative solutions. The results illustrated in Fig. 2 indicate that more than 90% of the contemporary CBR applications do not generate creative solutions. Among these non-creative CBR systems, more than 60% of them are either applied as mere case retrieval tools or directly reapply previous solutions to new problems. In the source one and source three, on an average, about 6% of CBR systems are able to provide creative solutions. Only about 2% of the existing CBR

123

386

Z. Zhu et al.

systems are described as the creative CBR systems developed particularly for the generation of creativity. Consequently, through this literature review of CBR applications, it suggests that CBR normally is not generating creative solutions.

3 Discussions The situation that CBR does not normally generate creative solutions can become an issue which impedes its potentials to be fully exploited. This issue can be manifested from following two perspectives: 1. Originated in 1970s (Schank and Abelson 1977), CBR received inspiration from human reasoning process (Leake 1996) and thus simulates people in remembering previous experience to deal with new situation. From the CBR point of view, human reasoning is primarily a process of remembering and manipulating one or a set of concrete instances. The decision is made on the comparison between the new situation and the old instance. The precedent instances are reminded and transferred to construct the solutions suitable to the new context. There is much evidence that people do, in fact, use case-based approaches in their daily reasoning (Kolodner 1993). However, if CBR does not generate creative solutions, it would not literarily reflect the reasoning when people are reminded with previous experience. On the basis of the case-based reminding-reapply process embodied in CBR, people seek creative solutions that are built upon the precedents in the past (Weisberg 2006) rather than generally repeat the previous solutions. As discussed earlier in Sect. 1.2, creativity is highly appreciated and treasured by people (Kaufman and Baer 2005). If it were not to consider and support the delivery of creative solutions, CBR could restrict itself as a case based retrieval tool and consequently have not lived up to its potential (Wills and Kolodner 1996). 2. On the basis of the research results in cognitive creativity, people generate creative solutions that are built upon the precedents in the past (Weisberg 2006). Previous problemsolving experience provides the foundation from which creativity can be derived. The ability to devise new ideas is greatly enhanced by having a wide, rich knowledge base (Clegg and Birch 2002). The more impressive the creativity, the more expert knowledge is typically demanded (Weisberg 2006). The importance of knowledge on creativity has also been revealed in literature (Chase and Simon 1973; Gruber 1974; Koestler 1975; Polanyi 1964; Rietzschel et al. 2007; Boden 2004; Bonnardel 2000; Ward 2007; Bailey et al. 2007; Vivacqua and de Souza 2004). In CBR, cases record concrete previous problem solving experience in contexts, which comprises the description of a problem, the process of solving the problem, and the solutions. This contextualised contents stored in CBR cases provide not just the information but also the scenarios and ways of manipulating and acting on that information. From this point of view, the contents of CBR cases are considered as knowledge. Therefore, the CBR’s capacity to remember precedent cases should be able to have much to offer in generating creative solutions (Wills and Kolodner 1996). The previous problem-solving experiences stored in CBR can be applied in interpreting the problem, understanding the situation in which the problem lies, supplying antecedents as the basis for the creation of new ideas.

123


387

4 Concluding remarks and future research In this paper, the creativity of CBR has been discussed. Through an extensive literature review on CBR applications, it is suggested that CBR does not normally generate creative solutions. According to the results of literature review, more than 60% of them are either applied as the case retrieval tools or simply reapply previous solutions to new problems; only about 2% of the existing CBR systems are deployed as the creative CBR systems particularly for the generation of creative solutions. Not normally generating creative solutions can be an issue for CBR. If not addressed, this can be an obstacle to realising the full potential of CBR methods. To address this issue, future research work may be undertaken to innovate the information processing scheme within the CBR method. On the basis of the research results in cognitive creativity, information processing, which means the ways how the information has been processed, is one of key components for generating creative solutions (Eder and Hosnedl 2008; Weisberg 2006). Creative engineers tend to mix algorithmic reasoning and heuristic searching, combine conceptual thinking and graphic coding and see useful connections and distinctions (Rouse 1986). However, the information processing currently conducted by CBR, as discussed earlier in this paper, are mainly the five-Re steps including case retrieval, reuse, revise, review, and retain. None of these five-Re processes is dedicated to generate creative solutions (Zhu et al. 2010). To innovate the information processing within the CBR method, the research achievement in the area of Creative Problem-Solving (CPS) methodologies can be adopted. Since 1960s, a large number of CPS models and methodologies have been developed. Some CPS techniques that have potential to be integrated with the CBR are briefly presented as follows. They are Combination, Divergence, Analogy, Generalisation, and Extencis. • Combination for creativity involves the merger of unfamiliar things into familiar ideas. In such cases of creative merging, two or more fairly common concepts, objects or complex mental structures are combined to produce a new structure, with its own new unity (Koestler 1975; Boden 2004; Ishikawa and Terano 1996). • Divergence in problem-solving is a mode of cognition that emphasises the revision of what is already known, exploring what would be known, and building new information (Piirto 2004). Divergence can produce numerous ideas among which creative concepts may exist (Guilford 1950; Csikszentmihalyi 1996; Simonton 1999). • Analogy is the correspondence among objects, which serves as a link for the creation of new objects (Merriam-Webster Inc 2008; Gael 1997). The literature on reasoning with analogy sometimes distinguish between within-domain analogy and cross-domain analogy (Vosniadou and Ortony 1989; Dunbar 1995). In creative process, knowledge needed to address a problem typically is not available to directly apply to the problem; however, knowledge can be obtained through analogy from other sources. • Generalisation represents objects in a general form which consists of the common features (Gael 1997). With generalisation, objects have been represented in simplified forms with key features, upon which rational methods and creative techniques can be adopted for the derivation of new concepts (Yan 1998). • Extenics (Cai et al. 2003; RIEE 2006) studies the transformation and extensibility of objects, rules, methods, and problem domains for revealing matters from various perspectives and then uses them for solving problems. Viewing a problem from various angles extends the conceptual spaces for searching and constructing a new solution, which subsequently can make problems being reviewed differently and addressed creatively.

123

388

Z. Zhu et al.

Once integrated appropriately with CBR methods, the selected CPS techniques can help guide the search for suitable previous cases as well as manipulate the information retrieved from the preceding cases in a way from which creative solutions may possibly be derived.

References Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun 7:39–59 Aha DW (1998) The omnipresence of case-based reasoning in science and application. Knowl Based Syst 11:261–273 Ahn H, Kim K-J, Han I (2007) A case-based reasoning system with the two-dimensional reduction technique for customer classification. Expert Syst Appl 32:1011–1019 Bailey AM, Mcdaniel WF, Thomas RK (2007) Approaches to the study of higher cognitive functions related to creativity in nonhuman animals. Methods 42:3–11 Bhatta S, Goel A (1997) A functional theory of design patterns. In: The international joint conference on artificial intelligence, Nagoya Bhatta S, Goel A (1998) Learning generic mechanisms for innovative strategies in adaptive design. Learn Sci 6:367–396 Boden MA (2004) The creative mind: myths and mechanisms. Routledge, London and New York Bohm D (2003) On creativity. Routledge, London Bonnardel N (2000) Towards understanding and supporting creativity in design: analogies in a constrained cognitive environment. Knowl Based Syst 13:505–513 Cai W, Yang C, Lin W (2003) Engineering extension methods. Science Press, Beijing Chase WG, Simon HA (1973) Perception in chess. Cogn Psychol 4:55–81 Clegg B, Birch P (2002) Crash course in creativity. Kogan Page, London Csikszentmihalyi M (1996) Creativity: flow and the psychology of discovery and invention. HarperCollins, New York Dunbar K (1995) How scientists really reason: scientific reasoning in real-world laboratories. In: Sternberg RJ, Davidson JE (eds) The nature of insight. MIT Press, Cambridge Eder WE, Hosnedl S (2008) Design engineering a manual for enhanced creativity. CRC Press, Boca Raton Faltings B, Sun K (1996) FAMING: supporting innovative mechanism shape design. Comput Aided Des 28:207–216 Frize M, Walker R (2000) Clinical decision-support systems for intensive care units using case-based reasoning. Med Eng Phys 22:671–677 Gael AK (1997) Design, analogy, and creativity. Expert, IEEE [see also IEEE Intell Syst Their Appl] 12:62–70 Garrelli Guiu JM, Golobardes i Ribé E, Bernadó i Mansilla E, Llorà i Fàbrega X (1999) Automatic diagnosis with genetic algorithms and case-based reasoning. Artif Intell Eng 3:367–372 Goel AK, Craw S (2006) Design, innovation and case-based reasoning. Knowl Eng Rev 20:271–276 Goel PS, Singh N (1998) Creativity and innovation in durable product development. Comput Ind Eng 35:5–8 Gomes P, Seco N, Pereira FC, Paiva P, Carreiro P, Ferreira JL, Bento C (2006) The importance of retrieval in creative design analogies. Knowl Based Syst 19:480–488 Gómez de Silva Garza A, Maher ML (1999) An evolutionary approach to case adaption. In: The 3rd international conference on case-based reasoning. Springer, Berlin Gruber HE (1974) Darwin on man: a psychological study of scientific creativity. Wildwood House, London Guilford JP (1950) Creativity. Am Psychol 5:444–454 Hoffmann O, Cropley DH, Cropley AJ, Nguyen L, Swatman P (2005) Creativity, requirements and perspectives. Aust J Inf Syst 13:159–175 Huang M-J, Chen M-Y, Lee S-C (2007) Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Syst Appl 32:856–867 Ishikawa T, Terano T (1996) Analogy by abstraction: case retrieval and adaptation for inventive design expert systems. Expert Syst Appl 10:351–356 Itkonen E (2005) Analogy as structure and process. John Benjamins Pub Co, Amsterdam Jo H, Han I, Lee H (1997) Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Syst Appl 13:97–108 Kaufman JC, Baer J (2005) Introduction: how people think, work, and act creatively in diverse domains. In: Kaufman JC, Baer JCreativity across domains. Lawrence Erlbaum Associates, Mahwah pp xiii–xvii Koestler A (1975) The act of creation. Picador, London Kolodner JL (1993) Case-based reasoning. Morgan Kaufmann Publishers, San Mateo

123


389

Kryssanov VV, Tamaki H, Kitamura S (1999) Towards a theory of creativity for engineering design. Systems, man, and cybernetics, 1999. In: IEEE SMC’99 conference proceedings, Tokyo Kryssanov VV, Tamaki H, Kitamura S (2001) Understanding design fundamentals: how synthesis and analysis drive creativity, resulting in emergence. Artif Intell Eng 15:329–342 Leake DB (1992) Evaluating explanations: a content theory. Erlbaum, Northvale Leake DB (1996) CBR in context: the present and future. In: Leake DB (ed) Case-based reasoning expriences, lessons, & future direction. AAAI Press; MIT Press, Menlo Park, Cambridge, pp 1–30 Lei Y, Peng Y, Ruan X (2001) Applying case-based reasoning to cold forging process planning. J Mater Process Technol 112:12–16 Li X, Yeh AG (2004) Multitemporal SAR images for monitoring cultivation systems using case-based reasoning. Remote Sens Environ 90:524–534 Mayer RE (1989) Cognitive views of creativity: creative teaching for creative learning. Contemp Educ Psychol 14:203–211 Mcgovern J, Samson D, Wirth A (1994) Using case based reasoning for basis development in intelligent decision systems. Eur J Oper Res 77:40–59 Merriam-Webster Inc (2008) Merriam-Webster online, the language center. Merriam-Webster Inc., Springfield Navinchandra D (1988) Case-based reasoning in CYCLOPS, a design problem solver. In: Kolodner JLWorkshop on case-based reasoning (DARPA), Clearwater, Florida. Morgan Kaufman, San Mateo Pajula E, Seuranen T, Koiranen T, Hurme M (2001) Synthesis of separation processes by using case-based reasoning. Comput Chem Eng 25:775–782 Piirto J (2004) Understanding creativity. Great Potential Press Inc., Scottsdale Plucker JA, Beghetto RA, Dow GT (2004) Why isn’t creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research. Educ Psychol 39:83–96 Poincaré H (1982) The foundations of science: science and hypothesis, the value of science, science and method. University Press of America, Washington Polanyi M (1964) Personal knowledge: towards a post-critical philosophy. Harper, New York Pope R (2005) Creativity: theory, history, practice. Routledge Abingdon Oxfordshire, New York Praehofer H, Kerschbaummayr J (1999) Case-based reasoning techniques to support reusability in a requirement engineering and system design tool. Eng Appl Artif Intell 12:717–731 Pulkkinen P, Laurikkala M, Ropponen A, Koivisto H (2008) Quality management in GPRS networks with fuzzy case-based reasoning. Knowl Based Syst 21:421–428 RIEE (2006) Brief introduction of extenics, extenics, avaliable via: http://web.gdut.edu.cn/~extenics/i.htm Riesbeck CK, Schank RC (1989) Inside case-based reasoning. Lawrence Erlbaum Associates, Northvale Rietzschel EF, Nijstad BA, Stroebe W (2007) Relative accessibility of domain knowledge and creativity: the effects of knowledge activation on the quantity and originality of generated ideas. J Exp Soc Psychol 43:933–946 Ross S, Fang L, Hipel KW (2002) A case-based reasoning system for conflict resolution: design and implementation. Eng Appl Artif Intell 15:369–383 Rouse WB (1986) A note on the nature of creativity in engineering: implications for supporting system design. Inf Proces Manag 22:279–285 Runco MA (2007) Creativity theories and themes: research, development, and practice. Elsevier, Amsterdam Schank RC, Abelson R (1977) Scripts, plans, goals and understanding. Lawrence Erlbaum Associates, Hillsdale Seo Y, Sheen D, Kim T (2007) Block assembly planning in shipbuilding using case-based reasoning. Expert Syst Appl 32:245–253 Seuranen T, Hurme M, Pajula E (2005) Synthesis of separation processes by case-based reasoning. Comput Chem Eng 29:1473–1482 Simonton DK (1999) Origin of genius: darwinian perspectives on creativity. Oxford University Press. Inc., Oxford, New York Stroulia E, Geol AK (1992) Genetic teleological mechanisms and their use in case adaptation. In: The 14th annual conference of the cognitive science, Northvale Termsinsuwan P, Cheng Z, Shiratori N (1996) A new approach to ADT specification support based on reuse of similar ADT by the application of case-based reasoning. Inf Softw Technol 38:555–568 Thomasson BJ, Ratcliffe MB, Thomas LA (2006) Improving the tutoring of software design using case-based reasoning. Adv Eng Inform 20:351–362 Tsai C-Y, Chiu C-C (2007) A case-based reasoning system for PCB principal process parameter identification. Expert Syst Appl 32:1183–1193 Tseng H-E, Chang C-C, Chang S-H (2005) Applying case-based reasoning for product configuration in mass customization environments. Expert Syst Appl 29:913–925

123

390

Z. Zhu et al.

Vivacqua AS, de Souza JM (2004) CreEx: a framework for creativity in cooperative problem solving. Groupw Des Implement Use 3198/2004:166–173 Vollrath I, Wilke W, Bergmann R (1998) Case-based reasoning support for online catalog sales. Internet Comput IEEE 2:47–54 Vong CM, Leung TP, Wong PK (2002) Case-based reasoning and adaptation in hydraulic production machine design. Eng Appl Artif Intell 15:567–585 Vosniadou S, Ortony A (1989) Similarity and analogical reasoning. Cambridge University Press, Cambridge Ward TB (2007) Creative cognition as a window on creativity. Methods 42:28–37 Watson I (1997) Applying case-based reasoning: techniques for enterprise system. Morgan Kaufmann Publishers Inc., San Francisco Watson I (2003) Understanding case-based reasoning, applying knowledge management. Morgan Kaufmann, San Francisco 23–46 Weisberg RW (2006) Creativity: understanding innovation in problem solving, science, invention, and the arts. Wiley, Hoboken Wills LM, Kolodner JL (1996) Towards more creative case-based design system. In: Leake DB (ed) Casebased reasoning expriences, lessons, & future direction. AAAI Press; MIT Press, Menlo Park; Cambridge, pp 81–91 Yan H-S (1998) Creative design of mechanical devices. Springer, Berlin Zhu Z, Hsu H-Y, Nagalingam S (2010) Towards Creative Case Based Reasoning. In: 2010 4th international conference on genetic and evolutionary computing, ShenZhen Zhuang ZY, Churilov L, Burstein F, Sikaris K (2009) Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners. Eur J Oper Res (in Press) corrected proof 195:662–675

123