Above are papers related to the following paper: [Paper_ID] Paper #110162. 	[Title] Case-Based Similarity Assessment: Estimating Adaptability from Experience [Abstract] Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily adaptable to fit current problems. However, widely-used similarity assessment strategies, such as evaluation of semantic similarity, can be poor predictors of adaptability. As a result, systems may select cases that are difficult or impossible for them to adapt, even when easily adaptable cases are available in memory. This paper presents a new similarity assessment approach which couples similarity judgments directly to a case library containing the system's adaptation knowledge. It examines this approach in the context of a case-based planning system that learns both new plans and new adaptations. Empirical tests of alternative similarity assessment strategies show that this approach enables better case selection and increases the benefits accrued from learned adaptations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1952. 	[Title] Combining Rules and Cases to Learn Case Adaptation [Abstract] Computer models of case-based reasoning (CBR) generally guide case adaptation using a fixed set of adaptation rules. A difficult practical problem is how to identify the knowledge required to guide adaptation for particular tasks. Likewise, an open issue for CBR as a cognitive model is how case adaptation knowledge is learned. We describe a new approach to acquiring case adaptation knowledge. In this approach, adaptation problems are initially solved by reasoning from scratch, using abstract rules about structural transformations and general memory search heuristics. Traces of the processing used for successful rule-based adaptation are stored as cases to enable future adaptation to be done by case-based reasoning. When similar adaptation problems are encountered in the future, these adaptation cases provide task- and domain-specific guidance for the case adaptation process. We present the tenets of the approach concerning the relationship between memory search and case adaptation, the memory search process, and the storage and reuse of cases representing adaptation episodes. These points are discussed in the context of ongoing research on DIAL, a computer model that learns case adaptation knowledge for case-based disaster response planning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1955. 	[Title] In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy Learning [Abstract] Machine learning techniques are perceived to have a great potential as means for the acquisition of knowledge; nevertheless, their use in complex engineering domains is still rare. Most machine learning techniques have been studied in the context of knowledge acquisition for well defined tasks, such as classification. Learning for these tasks can be handled by relatively simple algorithms. Complex domains present difficulties that can be approached by combining the strengths of several complementing learning techniques, and overcoming their weaknesses by providing alternative learning strategies. This study presents two perspectives, the macro and the micro, for viewing the issue of multistrategy learning. The macro perspective deals with the decomposition of an overall complex learning task into relatively well-defined learning tasks, and the micro perspective deals with designing multistrategy learning techniques for supporting the acquisition of knowledge for each task. The two perspectives are discussed in the context of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #337766. 	[Title] Learning to Refine Indexing by Introspective Reasoning [Abstract] A significant problem for case-based reasoning (CBR) systems is deciding what features to use in judging case similarity for retrieval. We describe research that addresses the feature selection problem by using introspective reasoning to learn new features for indexing. Our method augments the CBR system with an introspective reasoning component which monitors system performance to detect poor retrievals, identifies features which would lead to retrieving cases requiring less adaptation, and refines the indices to include such features in order to avoid similar future failures. We explore the benefit of introspective reasoning by performing empirical tests on the implemented system. These tests examine the benefits of introspective index refinement and the effects of problem order on case and index learning, and show that introspective learning of new index features improves overall performance across the range of different problem orders. 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 #70441. 	[Title] on Case-Based Reasoning Integrations Case-Based Seeding for an Interactive Crisis Response Assistant [Abstract] In this paper, we present an interactive, case-based approach to crisis response that provides users with the ability to rapidly develop good responses while allowing them to retain ultimate control over the decision-making process. We have implemented this approach in Inca, an INteractive Crisis Assistant for planning and scheduling in crisis domains. Inca relies on case-based methods to seed the response development process with initial candidate solutions drawn from previous cases. The human user then interacts with Inca to adapt these solutions to the current situation. We will discuss this interactive approach to crisis response using an artificial hazardous materials domain, Haz-Mat, that we developed for the purpose of evaluating candidate assistant mechanisms for crisis response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #83449. 	[Title] Context-Based Similarity Applied to Retrieval of Relevant Cases [Abstract] Retrieving relevant cases is a crucial component of case-based reasoning systems. The task is to use user-defined query to retrieve useful information, i.e., exact matches or partial matches which are close to query-defined request according to certain measures. The difficulty stems from the fact that it may not be easy (or it may be even impossible) to specify query requests precisely and completely resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for a large repositories which store various information (multifunctional information bases or a federated databases), a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise query specification and for changing the viewpoint. Efficient database techniques exists for locating exact matches. Finding relevant partial matches might be a problem. This document proposes a context-based similarity as a basis for flexible retrieval. Historical bacground on research in similarity assessment is presented and is used as a motivation for formal definition of context-based similarity. We also describe a similarity-based retrieval system for multifunctinal information bases. 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.

Above are papers related to the following paper: [Paper_ID] Paper #261040. 	[Title] Case-Based Learning: Beyond Classification of Feature Vectors [Abstract] The dominant theme of case-based research at recent ML conferences has been on classifying cases represented by feature vectors. However, other useful tasks can be targeted, and other representations are often preferable. We review the recent literature on case-based learning, focusing on alternative performance tasks and more expressive case representations. We also highlight topics in need of additional research. 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 #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 #3095. 	[Title] Constructive Similarity Assessment: Using Stored Cases to Define New Situations [Abstract] A fundamental issue in case-based reasoning is similarity assessment: determining similarities and differences between new and retrieved cases. Many methods have been developed for comparing input case descriptions to the cases already in memory. However, the success of such methods depends on the input case description being sufficiently complete to reflect the important features of the new situation, which is not assured. In case-based explanation of anomalous events during story understanding, the anomaly arises because the current situation is incompletely understood; consequently, similarity assessment based on matches between known current features and old cases is likely to fail because of gaps in the current case's description. Our solution to the problem of gaps in a new case's description is an approach that we call constructive similarity assessment. Constructive similarity assessment treats similarity assessment not as a simple comparison between fixed new and old cases, but as a process for deciding which types of features should be investigated in the new situation and, if the features are borne out by other knowledge, added to the description of the current case. Constructive similarity assessment does not merely compare new cases to old: using prior cases as its guide, it dynamically carves augmented descriptions of new cases out of memory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3097. 	[Title] Towards A Computer Model of Memory Search Strategy Learning [Abstract] Much recent research on modeling memory processes has focused on identifying useful indices and retrieval strategies to support particular memory tasks. Another important question concerning memory processes, however, is how retrieval criteria are learned. This paper examines the issues involved in modeling the learning of memory search strategies. It discusses the general requirements for appropriate strategy learning and presents a model of memory search strategy learning applied to the problem of retrieving relevant information for adapting cases in case-based reasoning. It discusses an implementation of that model, and, based on the lessons learned from that implementation, points towards issues and directions in refining the model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #218682. 	[Title] How to Retrieve Relevant Information? [Abstract] The document presents an approach to judging relevance of retrieved information based on a novel approach to similarity assessment. Contrary to other systems, we define relevance measures (context in similarity) at query time. This is necessary if since without a context in similarity one cannot guarantee that similar items will also be relevant. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3101. 	[Title] Acquiring Case Adaptation Knowledge: A Hybrid Approach [Abstract] The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10430. 	[Title] Rules and Precedents as Complementary Warrants Complementarity of Rules and Precedents for Classification In a [Abstract] This paper describes a model of the complementarity of rules and precedents in the classification task. Under this model, precedents assist rule-based reasoning by operationalizing abstract rule antecedents. Conversely, rules assist case-based reasoning through case elaboration, the process of inferring case facts in order to increase the similarity between cases, and term reformulation, the process of replacing a term whose precedents only weakly match a case with terms whose precedents strongly match the case. Fully exploiting this complementarity requires a control strategy characterized by impartiality, the absence of arbitrary ordering restrictions on the use of rules and precedents. An impartial control strategy was implemented in GREBE in the domain of Texas worker's compensation law. In a preliminary evaluation, GREBE's performance was found to be as good or slightly better than the performance of law students on the same task. A case is classified as belonging to a particular category by relating its description to the criteria for category membership. The justifications, or warrants [Toulmin, 1958], that can relate a case to a category, can vary widely in the generality of their antecedents. For example, consider warrants for classifying a case into the legal category "negligence." A rule, such as "An action is negligent if the actor fails to use reasonable care and the failure is the proximate cause of an injury," has very general antecedent terms (e.g., "breach of reasonable care"). Conversely, a precedent, such as "Dr. Jones was negligent because he failed to count sponges during surgery and as a result left a sponge in Smith," has very specific antecedent terms (e.g., "failure to count sponges"). Both types of warrants have been used by classification systems to relate cases to categories. Classification systems have used precedents to help match the antecedents of rules with cases. Completing this match is difficult when the terms in the antecedent are open-textured, i.e., when there is significant uncertainty whether they match specific facts [Gardner, 1984, McCarty and Sridharan, 1982]. This problem results from the "generality gap" separating abstract terms from specific facts [Porter et al., 1990]. Precedents of an open-textured term, i.e., past cases to which the term applied, can be used to bridge this gap. Unlike rule antecedents, the antecedents of precedents are at the same level of generality as cases, so no generality gap exists between precedents and new cases. Precedents therefore reduce the problem of matching specific case facts with open-textured terms to the problem of matching two sets of specific facts. For example, an injured employee's entitlement to worker's compensation depends on whether he was injured during an activity "in furtherance of employment." Determining whether any particular case should be classified as a compensable injury therefore requires matching the specific facts of the case (e.g., John was injured in an automobile accident while driving to his office) to the open-textured term "activity in furtherance of employment." The gap in generality between the case description and the abstract term makes this match problematical. However, completing this match may be much easier if there are precedents of the term "activity in furtherance of employment" (e.g., Mary's injury was not compensable because it occurred while she was driving to work, which is not an activity in furtherance of employment; Bill's injury was compensable because it occurred while he was driving to a house to deliver a pizza, an activity in furtherance of employment). In this case, John's driving to his office closely matches Mary's driving to work, so Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1951. 	[Title] Adaptive Similarity Assessment for Case-Based Explanation.* [Abstract]  Please summarise all the given information.

Paper #110162 has the title `Case-Based Similarity Assessment: Estimating Adaptability from Experience  
` and the abstract `Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily adaptable to fit current problems. However, widely-used similarity assessment strategies, such as evaluation of semantic similarity, can be poor predictors of adaptability. As a result, systems may select cases that are difficult or impossible for them to adapt, even when easily adaptable cases are available in memory. This paper presents a new similarity assessment approach which couples similarity judgments directly to a case library containing the system's adaptation knowledge. It examines this approach in the context of a case-based planning system that learns both new plans and new adaptations. Empirical tests of alternative similarity assessment strategies show that this approach enables better case selection and increases the benefits accrued from learned adaptations. 
`.
Question: Which category should Paper #110162 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #110162 has the title `Case-Based Similarity Assessment: Estimating Adaptability from Experience  
` and the abstract `Case-based problem-solving systems rely on similarity assessment to select stored cases whose solutions are easily adaptable to fit current problems. However, widely-used similarity assessment strategies, such as evaluation of semantic similarity, can be poor predictors of adaptability. As a result, systems may select cases that are difficult or impossible for them to adapt, even when easily adaptable cases are available in memory. This paper presents a new similarity assessment approach which couples similarity judgments directly to a case library containing the system's adaptation knowledge. It examines this approach in the context of a case-based planning system that learns both new plans and new adaptations. Empirical tests of alternative similarity assessment strategies show that this approach enables better case selection and increases the benefits accrued from learned adaptations. 
`.
Question: Which category should Paper #110162 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