Leake, D.B.: CBR in Context: The Present and Future. AAAI Press/MIT Press, Menlo Park (1996) 3--30  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Reasoning might be appropriate. 3 Case Based Reasoning Case Based Reasoning (CBR) is a problem solving strategy based on reusing experience gained in previous problem solving episodes [16] It can be e ective in domains in which problem types recur and similar problems have similar solutions [17]. In CBR, a new problem is not generally solved by reasoning from scratch . Instead, it is solved by retrieving from a memory the solutions to similar previously solved problems, and transferring and adapting these solutions to the new problem. The memory is referred to as a case base, and each ....

Leake, D.B.: CBR in Context: The Present and Future, in D.B.Leake (ed.), CaseBased Reasoning | Experiences, Lessons & Future Directions, pp.3-30, AAAI Press, 1996.

....and constraints that should guide addition and deletion of cases. They illustrate the importance of augmenting competence based criteria with quantitative performance based considerations, and propose a strategy for reflecting adaptation performance effects when compressing a case base. Leake [9] states that a case based reasoner only needs to handle the types of problems that actually occur in practice, while generative systems must account for all possible problems. So, another check might be to look if there are no superfluous cases. Uncontrolled case base growth can cause serious ....

David Leake. CBR in context: The present and the future. In Case--Based Reasoning: Experiences, Lessons, and Future Directions [8], pages 3--30.

....case and other cases in the p dimensional feature space. 4 The most similar cases or projects are then used, possibly with adaptation to generate a prediction for the target case. Once the target case has completed it can be added to the case base. A more general account of CBR may be found in [8]. ANGEL is an analogy based estimation tool that searches for similarities between a target entity, such as a proposed software project, and a set of historical entities of the same class. Each entity is characterised by a number of attributes that are available at the point when estimates are ....

Leake, D., CBR in context: the present and the future, in Case based reasoning: experiences, lessons and future directions, D. Leake, Editor, AAAI Press: Menlo Park, 1996.

.... so customers need support. One kind of knowledge that turned out to be very useful is the knowledge about the similarity of products [VW98] Similarity representation and similarity based search techniques have been investigated in detail in the CBR context (a good introduction is given in [L96]) In the context of e commerce, the user s entries describing his ideal product might be seen as a specification of a new problem. He does this by specifying concrete values for product attributes. The CBR system searches for appropriate (similar) products in the database, evaluates the results ....

Leake, D., 1996, CBR in Context: The Present and Future. In: Leake D (ed) 1996 sl Case-Based Reasoning, Experiences, Lessons & Future Directions. MIT Press, Menlo Park, Calif, pp 3-30

.... Based Reasoning (CBR) CBR is a computer technique, which combines the knowledge based support philosophy with a simulation of human reasoning when past experience is used, i.e. mentally searching for similar situations happened in the past and reusing the experience gained in those situations [8]. In the same way, in CBR, the knowledge cases are structured and stored in a database, which the user queries when trying to solve a problem. The system retrieves a set of similar cases and then evaluates the similarity between each case in the database and the query. The most similar case(s) are ....

D. B. Leake. CBR in Context: The Present and Future. In D. Leake's Case-Based Reasoning-- Experiences, Lessons, & Future Directions. AAAI Press / MIT Press, 1996.

....and other cases in the p dimensional feature space. The most similar cases or projects are then used, possibly with adaptation to generate a prediction for the target case. Once the target case has completed it can be added to the case base. A more general account of CBR may be found in Leake [14]. The approach of Vicinanza et al. is to use domain specific similarity measures (mainly based on size e.g. LOC and function points) coupled with rule based adaptation. The authors extract the rules by analysing a protocol of an expert estimating project effort for hypothetical projects. This has ....

Leake, D., CBR in context: the present and the future, in Case based reasoning: experiences, lessons and future directions, D. Leake, Editor, AAAI Press: Menlo Park, 1996.

....and Constraint Logic Programming might yield a solution to the problem and one that also improved on earlier attempts in a number of ways. CBR and CLP are both mechanisms that hold out the possibility of reduction of the search space in complex problems by prior elimination of some possibilities [12]. CBR has apparently not been applied in this area before so it was decided to explore its possibilities. It was also thought that a combination of the two (CBR CLP) might reduce the search space even further. Subsequent experiments have been undertaken to test that hypothesis, using a nurse ....

....by Cheng et al. [1] Supplementing these background ideas the authors interviewed nurses and observed the manual preparation of rosters. This led to a better understanding of the way in which the human mind approached the solution of such problems. It seemed to tie in with a case based approach [12] supplemented by a rule and constraint based adaptation (fix) mechanism. It is this that has led to the development of software that attempts to copy that approach. 2.2 The Basic Steps Involved in Solution In the current implementation of the approach set out in this paper, the sequence of steps ....

Leake, D.B. "CBR in Context: The Present and the Future", in Case-Based Reasoning: Experiences, Lessons and Future Directions, Menlo Park: AAAI Press 1996

....and the robotic skill acquisition system of Handelman [34] Neural Computing Surveys 2, 62 93, 1999, http: www.icsi.berkeley.edu jagota NCS 85 5. 5 Parallel interleaved integration in a CBR NN data analysis system The hybrid system developed by Lees et al. [56] uses a case based reasoning (CBR) [1, 55] module interleaved with several neural network modules. The system requires interleaved integration since the neural network modules undergo fine grained, on line manipulation of their parameters. The system is intended as a tool for data mining and knowledge discovery within large databases. It ....

D. B. Leake. CBR in context: the present and future, AAAI Press/MIT Press, California, 1996.

....users and experts. By incorporating product knowledge into the database it is much more flexible and powerful than just offering both menu driven product selection and a searchable database. 4 The Concept of Case Based Reasoning The basic idea of Case Based Reasoning (a good overview is given in [2]) is to solve new problems by comparing them to old problems that already have been solved in the past (Fig. 1) The key assumption is that if two problems look similar, then the solutions to these problems are often similar as well. However, CBR systems must know how to decide whether two cases ....

Leake D 1996 CBR in Context: The Present and Future. In: Leake D (ed) 1996 Case-Based Reasoning, Experiences, Lessons, & Future Directions. MIT Press, Menlo Park, Calif, pp 3-30

....of research. 3 Case Based Reasoning Case Based Reasoning is a problem solving methodology by which new solutions are generated by retrieving the most relevant past experiences (cases) from memory, and adapting them to fit new situations. In short, it is sometimes called reasoning by remembering [19]. 3.1 Motivation for CBR To understand why dynamic CSP fits naturally into a CBR framework, we must first understand the motivation behind case based reasoning. The motivation behind problem solving with CBR is based on two tenets of the world, as described by Leake [19] The first is that the ....

....reasoning by remembering [19] 3.1 Motivation for CBR To understand why dynamic CSP fits naturally into a CBR framework, we must first understand the motivation behind case based reasoning. The motivation behind problem solving with CBR is based on two tenets of the world, as described by Leake [19]. The first is that the world is regular. This means that similar problems will have similar solutions, and leads us to the conclusion that solutions for similar problems are a useful starting point for new problem solving. The second tenet of the world is that the types of problems encountered ....

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David Leake. Cbr in context: The present and future. In Case Based Reasoning: Experiences, Lessons, and Future Directions, 1995.

....is maintained. Further it does not require de compression before anything useful can be achieved. 2 Previous Work Central to CBR is the concept of similarity [ Kolodner, 1993 ] Cases may be considered as multi dimensional objects existing within a multi dimensional space the case space [ Leake, 1996 ] as illustrated in Figure 1. Case objects that are similar to each other reside in close proximity to each other within the case space. Case retrieval involves conceptually positioning a target case in the case space and finding those cases close (similar) to it. The case space is a manifestation ....

D.B. Leake. Cbr in context: The present and future. In D.B. Leake, editor, Case-Based Reasoning: Experiences, Lessons and Future Directions, pages 3--30, 1996.

....Databases This article discusses possibilities to improve the usefulness of electronic catalogs to the user. We give examples of how difficult it is often to find desired information or products in electronic catalogs or product databases. We introduce the concept of Case Based Reasoning (CBR) [1, 5] that solves many of the problems associated with ordinary database queries. We present some of the first online catalogs that already use CBR technology and we take a closer look at a selected case study. We summarize the benefits of current CBR catalogs over other approaches and finally we ....

....users and experts. By incorporating product knowledge into the database it is much more flexible and powerful than just offering both menu driven product selection and a searchable database. 3 The Concept of Case Based Reasoning The basic idea of Case Based Reasoning (a good overview is given in [5]) is to solve new problems by comparing them to old problems that already have been solved in the past (Figure 1) The key assumption is that if two problems look similar, then the solutions to these problems are often similar as well. However, CBR systems must know how to decide whether two cases ....

[Article contains additional citation context not shown here]

D. Leake. 1996. CBR in Context: The Present and Future. In Case-Based Reasoning, Experiences, Lessons, & Future Directions. ed. D. Leake, 3-30. Menlo Park, Calif. MIT Press.

....3, and we conclude with a summary in Section 4. Case Adaptation as a Constraint Satisfaction Problem Adaptation is the component of a case based reasoning system that adapts retrieved cases to fit new circumstances. It is often considered to be the most difficult problem in case based reasoning (Leake 1995). In order to enable an efficient and systematic adaptation process that combines several cases, we developed a case combination methodology in which each existing case is represented as a discrete CSP. The methodology then combines the cases that match the new problem situation into a valid ....

Leake, D. 1995. Cbr in context: The present and future.

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Leake, D.B.: CBR in Context: The Present and Future. AAAI Press/MIT Press, Menlo Park (1996) 3--30

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Leake, D.B., ed.: CBR in Context: The Present and Future. In: Case-based Reasoning: Experiences, Lessons and Future Directions. Menlo Park: AAAI Press MIT Press (1996) 1--25

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Leake, D., B., CBR in Context: the Present and Future, in Leake D. B. (ed) Case-Based Reasoning, in Leake, D. B. (ed) Case-Based Reasoning: Experiences, Lessons and Future Directions, pp349-370, MIT Press, (1996).

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David B. Leake. CBR in context: The present and future. In David B. Leake, editor, Case-Based Reasoning - Experiences, Lessons, & Future Directions, chapter 1. AAAI Press, 1996.

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Leake D.B.: CBR in Context: The Present and Future. In Leake D. (ed.): Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press/MIT Press, 1996.

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Leake D.B.: CBR in Context: The Present and Future. In Leake D. (ed.): Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press/MIT Press, 1996.

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Leake, D. D. [1996]. CBR in Context: The Present and Future. In Leake, D. B. (ed.). Case-Based Reasoning. Experiences, Lessons & Future Directions Cambridge, MA: AAAI Press/ MIT Press

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