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.

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 #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 #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 #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 #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 #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.

Paper #1951 has the title `Adaptive Similarity Assessment for Case-Based Explanation.*  
` and the abstract `nan`.
Question: Which category should Paper #1951 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1951 has the title `Adaptive Similarity Assessment for Case-Based Explanation.*  
` and the abstract `nan`.
Question: Which category should Paper #1951 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