Above are papers related to the following paper: [Paper_ID] Paper #1110024. 	[Title] Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning [Abstract] Conceptual analogy (CA) is a general approach that applies conceptual clustering and concept representations to facilitate the efficient use of past experiences (cases) during analogical reasoning (Borner 1995). The approach was developed and implemented in SYN* (see also (Borner 1994, Borner and Faauer 1995)) to support the design of supply nets in building engineering. This paper sketches the task; it outlines the nearest-neighbor-based agglomerative conceptual clustering applied in organizing large amounts of structured cases into case classes; it provides the concept representation used to characterize case classes and shows the analogous solution of new problems based on the concepts available. However, the main purpose of this paper is to evaluate CA in terms of its reasoning efficiency; its capability to derive solutions that go beyond the cases in the case base but still preserve the quality of cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153891. 	[Title] Conceptual Analogy [Abstract] Conceptual analogy (CA) is an approach that integrates conceptualization, i.e., memory organization based on prior experiences and analogical reasoning (Borner 1994a). It was implemented prototypically and tested to support the design process in building engineering (Borner and Janetzko 1995, Borner 1995). There are a number of features that distinguish CA from standard approaches to CBR and AR. First of all, CA automatically extracts the knowledge needed to support design tasks (i.e., complex case representations, the relevance of object features and relations, and proper adaptations) from attribute-value representations of prior layouts. Secondly, it effectively determines the similarity of complex case representations in terms of adaptability. Thirdly, implemented and integrated into a highly interactive and adaptive system architecture it allows for incremental knowledge acquisition and user support. This paper surveys the basic assumptions and the psychological results which influenced the development of CA. It sketches the knowledge representation formalisms employed and characterizes the sub-processes needed to integrate memory organization and analogical reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #175909. 	[Title] Towards Formalizations in Case-Based Reasoning for Synthesis [Abstract] This paper presents the formalization of a novel approach to structural similarity assessment and adaptation in case-based reasoning (Cbr) for synthesis. The approach has been informally presented, exemplified, and implemented for the domain of industrial building design (Borner 1993). By relating the approach to existing theories we provide the foundation of its systematic evaluation and appropriate usage. Cases, the primary repository of knowledge, are represented structurally using an algebraic approach. Similarity relations provide structure preserving case modifications modulo the underlying algebra and an equational theory over the algebra (so available). This representation of a modeled universe of discourse enables theory-based inference of adapted solutions. The approach enables us to incorporate formally generalization, abstraction, geometrical transformation, and their combinations into Cbr. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #166825. 	[Title] A Preprocessing Model for Integrating CBR and Prototype-Based Neural Networks [Abstract] Some important factors that play a major role in determining the performances of a CBR (Case-Based Reasoning) system are the complexity and the accuracy of the retrieval phase. Both flat memory and inductive approaches suffer from serious drawbacks. In the first approach, the search time increases when dealing with large scale memory base, while in the second one the modification of the case memory becomes very complex because of its sophisticated architecture. In this paper, we show how we construct a simple efficient indexing system structure. The idea is to construct a case hierarchy with two levels of memory: the lower level contains cases organised into groups of similar cases, while the upper level contains prototypes. each prototype represents one group of cases. This smaller memory is used during the retrieval phase. Prototype construction is achieved by means of an incremental prototype-based NN (Neural Network). We show that this mode of CBR-NN coupling is a preprocessing one where the neural network serves as an indexing system to the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3085. 	[Title] MAC/FAC: A Model of Similarity-based Retrieval [Abstract] We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical re-mindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data. 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 #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.

Above are papers related to the following paper: [Paper_ID] Paper #1152564. 	[Title] S o l u t i o n Relevant A b s t r a [Abstract] Two major problems in case-based reasoning are the efficient and justified retrieval of source cases and the adaptation of retrieved solutions to the conditions of the target. For analogical theorem proving by induction, we describe how a solution-relevant abstraction can restrict the retrieval of source cases and the mapping from the source problem to the target problem and how it can determine reformulations that further adapt the source solution. 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 #221302. 	[Title] Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems [Abstract] We present a framework for task-driven knowledge acquisition in the development of design support systems. Different types of knowledge that enter the knowledge base of a design support system are defined and illustrated both from a formal and from a knowledge acquisition vantage point. Special emphasis is placed on the task-structure, which is used to guide both acquisition and application of knowledge. Starting with knowledge for planning steps in design and augmenting this with problem-solving knowledge that supports design, a formal integrated model of knowledge for design is constructed. Based on the notion of knowledge acquisition as an incremental process we give an account of possibilities for problem solving depending on the knowledge that is at the disposal of the system. Finally, we depict how different kinds of knowledge interact in a design support system. ? This research was supported by the German Ministry for Research and Technology (BMFT) within the joint project FABEL under contract no. 413-4001-01IW104. Project partners in FABEL are German National Research Center of Computer Science (GMD), Sankt Augustin, BSR Consulting GmbH, Munchen, Technical University of Dresden, HTWK Leipzig, University of Freiburg, and University of Karlsruhe. Please summarise all the given information.

Paper #1110024 has the title `Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning  
` and the abstract `Conceptual analogy (CA) is a general approach that applies conceptual clustering and concept representations to facilitate the efficient use of past experiences (cases) during analogical reasoning (Borner 1995). The approach was developed and implemented in SYN* (see also (Borner 1994, Borner and Faauer 1995)) to support the design of supply nets in building engineering. This paper sketches the task; it outlines the nearest-neighbor-based agglomerative conceptual clustering applied in organizing large amounts of structured cases into case classes; it provides the concept representation used to characterize case classes and shows the analogous solution of new problems based on the concepts available. However, the main purpose of this paper is to evaluate CA in terms of its reasoning efficiency; its capability to derive solutions that go beyond the cases in the case base but still preserve the quality of cases.
`.
Question: Which category should Paper #1110024 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1110024 has the title `Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning  
` and the abstract `Conceptual analogy (CA) is a general approach that applies conceptual clustering and concept representations to facilitate the efficient use of past experiences (cases) during analogical reasoning (Borner 1995). The approach was developed and implemented in SYN* (see also (Borner 1994, Borner and Faauer 1995)) to support the design of supply nets in building engineering. This paper sketches the task; it outlines the nearest-neighbor-based agglomerative conceptual clustering applied in organizing large amounts of structured cases into case classes; it provides the concept representation used to characterize case classes and shows the analogous solution of new problems based on the concepts available. However, the main purpose of this paper is to evaluate CA in terms of its reasoning efficiency; its capability to derive solutions that go beyond the cases in the case base but still preserve the quality of cases.
`.
Question: Which category should Paper #1110024 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