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 #77826. 	[Title] Merge Strategies for Multiple Case Plan Replay [Abstract] Planning by analogical reasoning is a learning method that consists of the storage, retrieval, and replay of planning episodes. Planning performance improves with the accumulation and reuse of a library of planning cases. Retrieval is driven by domain-dependent similarity metrics based on planning goals and scenarios. In complex situations with multiple goals, retrieval may find multiple past planning cases that are jointly similar to the new planning situation. This paper presents the issues and implications involved in the replay of multiple planning cases, as opposed to a single one. Multiple case plan replay involves the adaptation and merging of the annotated derivations of the planning cases. Several merge strategies for replay are introduced that can process with various forms of eagerness the differences between the past and new situations and the annotated justifications at the planning cases. In particular, we introduce an effective merging strategy that considers plan step choices especially appropriate for the interleaving of planning and plan execution. We illustrate and discuss the effectiveness of the merging strategies in specific domains. 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 #77829. 	[Title] Towards Mixed-Initiative Rationale-Supported Planning [Abstract] This paper introduces our work on mixed-initiative, rationale-supported planning. The work centers on the principled reuse and modification of past plans by exploiting their justification structure. The goal is to record as much as possible of the rationale underlying each planning decision in a mixed-initiative framework where human and machine planners interact. This rationale is used to determine which past plans are relevant to a new situation, to focus user's modification and replanning on different relevant steps when external circumstances dictate, and to ensure consistency in multi-user distributed scenarios. We build upon our previous work in Prodigy/Analogy, which incorporates algorithms to capture and reuse the rationale of an automated planner during its plan generation. To support a mixed-initiative environment, we have developed user interactive capabilities in the Prodigy planning and learning system. We are also working towards the integration of the rationale-supported plan reuse in Prodigy/Analogy with the plan retrieval and modification tools of ForMAT. Finally, we have focused on the user's input into the process of plan reuse, in particular when conditional planning is needed. Please summarise all the given information.

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 #103529. 	[Title] On the Usefulness of Re-using Diagnostic Solutions [Abstract] Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the same kind of results apply also for diagnosis. We propose a theoretical complexity analysis coupled with some experimental tests, intended to evaluate the adequacy of adaptation strategies which re-use the solutions of past diagnostic problems in order to build a solution to the problem to be solved. Results of such analysis show that, even if diagnosis re-use falls into the same complexity class of diagnosis generation (they are both NP-complete problems), practical advantages can be obtained by exploiting a hybrid architecture combining case-based and model-based diagnostic problem solving in a unifying framework. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3112. 	[Title] Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning [Abstract] Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information. 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 #470511. 	[Title] Supporting Combined Human and Machine Planning: The Prodigy 4.0 User Interface Version 2.0* [Abstract] Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potential of achieving better plans than either the human or the machine can create alone. Human planners often take a case-based approach to planning, relying on their past experience and planning by retrieving and adapting past planning cases. Planning by analogical reasoning in which generative and case-based planning are combined, as in Prodigy/Analogy, provides a suitable framework to study this mixed-initiative integration. However, having a human user engaged in this planning loop creates a variety of new research questions. The challenges we found creating a mixed-initiative planning system fall into three categories: planning paradigms differ in human and machine planning; visualization of the plan and planning process is a complex, but necessary task; and human users range across a spectrum of experience, both with respect to the planning domain and the underlying planning technology. This paper presents our approach to these three problems when designing an interface to incorporate a human into the process of planning by analogical reasoning with Prodigy/Analogy. The interface allows the user to follow both generative and case-based planning, it supports visualization of both plan and the planning rationale, and it addresses the variance in the experience of the user by allowing the user to control the presentation of information. * This research is sponsored as part of the DARPA/RL Knowledge Based Planning and Scheduling Initiative under grant number F30602-95-1-0018. A short version of this document appeared as Cox, M. T., & Veloso, M. M. (1997). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In D. B. Leake & E. Plaza (Eds.), Case-Based Reasoning Research and Development: Second International Conference on Case-Based Reasoning (pp. 531-540). Berlin: Springer-Verlag. 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 #110163. 	[Title] Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning [Abstract] The case-based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the different learning mechanisms interact and their combined utility. This paper presents a case study examining the relative contributions and costs involved in learning processes for three different knowledge sources|cases, case adaptation knowledge, and similarity information|in a case-based planner. It demonstrates the importance of interactions between different learning processes and identifies a promising method for integrating multiple learning methods to improve case-based reasoning. 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.

Above are papers related to the following paper: [Paper_ID] Paper #110164. 	[Title] A Case Study of Case-Based CBR [Abstract] Case-based reasoning depends on multiple knowledge sources beyond the case library, including knowledge about case adaptation and criteria for similarity assessment. Because hand coding this knowledge accounts for a large part of the knowledge acquisition burden for developing CBR systems, it is appealing to acquire it by learning, and CBR is a promising learning method to apply. This observation suggests developing case-based CBR systems, CBR systems whose components themselves use CBR. However, despite early interest in case-based approaches to CBR, this method has received comparatively little attention. Open questions include how case-based components of a CBR system should be designed, the amount of knowledge acquisition effort they require, and their effectiveness. This paper investigates these questions through a case study of issues addressed, methods used, and results achieved by a case-based planning system that uses CBR to guide its case adaptation and similarity assessment. The paper discusses design considerations and presents empirical results that support the usefulness of case-based CBR, that point to potential problems and tradeoffs, and that directly demonstrate the overlapping roles of different CBR knowledge sources. The paper closes with general lessons about case-based CBR and areas for future research. 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 #1949. 	[Title] Learning to Improve Case Adaptation by Introspective Reasoning and CBR [Abstract] In current CBR systems, case adaptation is usually performed by rule-based methods that use task-specific rules hand-coded by the system developer. The ability to define those rules depends on knowledge of the task and domain that may not be available a priori, presenting a serious impediment to endowing CBR systems with the needed adaptation knowledge. This paper describes ongoing research on a method to address this problem by acquiring adaptation knowledge from experience. The method uses reasoning from scratch, based on introspective reasoning about the requirements for successful adaptation, to build up a library of adaptation cases that are stored for future reuse. We describe the tenets of the approach and the types of knowledge it requires. We sketch initial computer implementation, lessons learned, and open questions for further study. Please summarise all the given information.

Paper #1152564 has the title `S o l u t i o n Relevant A b s t r a
` and the 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.
`.
Question: Which category should Paper #1152564 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1152564 has the title `S o l u t i o n Relevant A b s t r a
` and the 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.
`.
Question: Which category should Paper #1152564 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