Above are papers related to the following paper: [Paper_ID] Paper #1116146. 	[Title] Fish and Shrink. A next step towards e-cient case retrieval in large scaled case bases [Abstract] Keywords: Case-Based Reasoning, case retrieval, case representation This paper deals with the retrieval of useful cases in case-based reasoning. It focuses on the questions of what "useful" could mean and how the search for useful cases can be organized. We present the new search algorithm Fish and Shrink that is able to search quickly through the case base, even if the aspects that deflne usefulness are spontaneously combined at query time. We compare Fish and Shrink to other algorithms and show that most of them make an implicit closed world assumption. We flnally refer to a realization of the presented idea in the context of the prototype of the FABEL-Project 1 . The scenery is as follows. Previously collected cases are stored in a large scaled case base. An expert describes his problem and gives the aspects in which the requested case should be similar. The similarity measure thus given spontaneously shall now be used to explore the case base within a short time, shall present a required number of cases and make sure that none of the other cases is more similar. The question is now how to prepare the previously collected cases and how to deflne a retrieval algorithm which is able to deal with sponta neously user-deflned similarity measures. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15889. 	[Title] Structural Similarity as Guidance in Case-Based Design [Abstract] This paper presents a novel approach to determine structural similarity as guidance for adaptation in case-based reasoning (Cbr). We advance structural similarity assessment which provides not only a single numeric value but the most specific structure two cases have in common, inclusive of the modification rules needed to obtain this structure from the two cases. Our approach treats retrieval, matching and adaptation as a group of dependent processes. This guarantees the retrieval and matching of not only similar but adaptable cases. Both together enlarge the overall problem solving performance of Cbr and the explainability of case selection and adaptation considerably. Although our approach is more theoretical in nature and not restricted to a specific domain, we will give an example taken from the domain of industrial building design. Additionally, we will sketch two prototypical implementations of this approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15892. 	[Title] Automatic Indexing, Retrieval and Reuse of Topologies in Architectual Layouts [Abstract] Former layouts contain much of the know-how of architects. A generic and automatic way to formalize this know-how in order to use it by a computer would save a lot of effort and money. However, there seems to be no such way. The only access to the know-how are the layouts themselves. Developing a generic software tool to reuse former layouts you cannot consider every part of the architectual domain or things like personal style. Tools used today only consider small parts of the architectual domain. Any personal style is ignored. Isn't it possible to build a basic tool which is adjusted by the content of the former layouts, but may be extended incremently by modeling as much of the domain as desirable? This paper will describe a reuse tool to perform this task focusing on topological and geometrical binary relations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118388. 	[Title] Structural Similarity and Adaptation [Abstract] Most commonly, case-based reasoning is applied in domains where attribute value representations of cases are sufficient to represent the features relevant to support classification, diagnosis or design tasks. Distance functions like the Hamming-distance or their transformation into similarity functions are applied to retrieve past cases to be used to generate the solution of an actual problem. Often, domain knowledge is available to adapt past solutions to new problems or to evaluate solutions. However, there are domains like architectural design or law in which structural case representations and corresponding structural similarity functions are needed. Often, the acquisition of adaptation knowledge seems to be impossible or rather requires an effort that is not manageable for fielded applications. Despite of this, humans use cases as the main source to generate adapted solutions. How to achieve this computationally? This paper presents a general approach to structural similarity assessment and adaptation. The approach allows to explore structural case representations and limited domain knowledge to support design tasks. It is exemplarily instantiated in three modules of the design assistant FABEL-Idea that generates adapted design solutions on the basis of prior CAD layouts. Please summarise all the given information.

Paper #1116146 has the title `Fish and Shrink. A next step towards e-cient case retrieval in large scaled case bases  
` and the abstract `Keywords: Case-Based Reasoning, case retrieval, case representation This paper deals with the retrieval of useful cases in case-based reasoning. It focuses on the questions of what "useful" could mean and how the search for useful cases can be organized. We present the new search algorithm Fish and Shrink that is able to search quickly through the case base, even if the aspects that deflne usefulness are spontaneously combined at query time. We compare Fish and Shrink to other algorithms and show that most of them make an implicit closed world assumption. We flnally refer to a realization of the presented idea in the context of the prototype of the FABEL-Project 1 . The scenery is as follows. Previously collected cases are stored in a large scaled case base. An expert describes his problem and gives the aspects in which the requested case should be similar. The similarity measure thus given spontaneously shall now be used to explore the case base within a short time, shall present a required number of cases and make sure that none of the other cases is more similar. The question is now how to prepare the previously collected cases and how to deflne a retrieval algorithm which is able to deal with sponta neously user-deflned similarity measures.
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Question: Which category should Paper #1116146 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1116146 has the title `Fish and Shrink. A next step towards e-cient case retrieval in large scaled case bases  
` and the abstract `Keywords: Case-Based Reasoning, case retrieval, case representation This paper deals with the retrieval of useful cases in case-based reasoning. It focuses on the questions of what "useful" could mean and how the search for useful cases can be organized. We present the new search algorithm Fish and Shrink that is able to search quickly through the case base, even if the aspects that deflne usefulness are spontaneously combined at query time. We compare Fish and Shrink to other algorithms and show that most of them make an implicit closed world assumption. We flnally refer to a realization of the presented idea in the context of the prototype of the FABEL-Project 1 . The scenery is as follows. Previously collected cases are stored in a large scaled case base. An expert describes his problem and gives the aspects in which the requested case should be similar. The similarity measure thus given spontaneously shall now be used to explore the case base within a short time, shall present a required number of cases and make sure that none of the other cases is more similar. The question is now how to prepare the previously collected cases and how to deflne a retrieval algorithm which is able to deal with sponta neously user-deflned similarity measures.
`.
Question: Which category should Paper #1116146 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Case_Based