Abstract:Case-based reasoning (CBR) is an AI technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, and for this reason the strategy of reusing multiple cases is immediately appealing. This paper describ… Show more
“…CBR helps decision theory handle complicated problems with many variables and alternatives (Tsatsoulis, Cheng, & Wei, 1997). Smyth et al (2001) applied a hierarchical problem-solving paradigm to CBR where the problem domain is highly complicated. INRECA performs classification by a decision tree, which is generated from the case base or a matched or similar concept.…”
“…CBR helps decision theory handle complicated problems with many variables and alternatives (Tsatsoulis, Cheng, & Wei, 1997). Smyth et al (2001) applied a hierarchical problem-solving paradigm to CBR where the problem domain is highly complicated. INRECA performs classification by a decision tree, which is generated from the case base or a matched or similar concept.…”
“…For instance in case x i = (x i1 , ..., x if , ..., x i|F | ) the feature x if could reference a case-structure. This simple extension allows for the description of cases with a complex hierarchical structure [8,10,12,13].…”
Assessing the similarity between cases is a key aspect of the retrieval phase in casebased reasoning (CBR). In most CBR work, similarity is assessed based on feature-value descriptions of cases using similarity metrics which use these feature values. In fact it might be said that this notion of a feature-value representation is a defining part of the CBR world-view -it underpins the idea of a problem space with cases located relative to each other in this space. Recently a variety of similarity mechanisms have emerged that are not founded on this feature-space idea. Some of these new similarity mechanisms have emerged in CBR research and some have arisen in other areas of data analysis. In fact research on Support Vector Machines (SVM) is a rich source of novel similarity representations because of the emphasis on encoding domain knowledge in the kernel function of the SVM. In this paper we present a taxonomy that organises these new similarity mechanisms and more established similarity mechanisms in a coherent framework.
“…Different researchers in different design domains have arrived at their own accounts of how analogy-based design happens in their respective domains. For instance, [8] and [5] provide examples of analogy-based design in architecture; [11], and [3] provide an account of analogy-based design in electro-mechanical device design; [12] provides an account analogy-based design in the domain of software design; etc.…”
Section: Compound Analogical Design: a Conceptual Frameworkmentioning
Biologically inspired design (BID) can be viewed as an example of analogy-based design. Existing models of analogy-based design do not fully account for the generation of complex solutions in BID, especially those which contain compound solutions. In this paper we develop a conceptual framework of compound analogical design that explains the generation of compound solutions in design through opportunistic interaction of two related processes: analogical transfer and problem decomposition. We apply this framework to analyze three sample biologically inspired designs that contain compound solutions.
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