Above are papers related to the following paper: [Paper_ID] Paper #89308. 	[Title] Focusing Construction and Selection of Abductive Hypotheses [Abstract] Many abductive understanding systems explain novel situations by a chaining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive hypotheses are focused by specific explanations of prior episodes and by goal-based criteria reflecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday domains. We identify five central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in domains that are complex and imperfectly un derstood. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107171. 	[Title] A utility-based approach to learning in a mixed Case-Based and Model-Based Reasoning architecture [Abstract] Case-based reasoning (CBR) can be used as a form of "caching" solved problems to speedup later problem solving. Using "cached" cases brings additional costs with it due to retrieval time, case adaptation time and also storage space. Simply storing all cases will result in a situation in which retrieving and trying to adapt old cases will take more time (on average) than not caching at all. This means that caching must be applied selectively to build a case memory that is actually useful. This is a form of the utility problem [4, 2]. The approach taken here is to construct a "cost model" of a system that can be used to predict the effect of changes to the system. In this paper we describe the utility problem associated with "caching" cases and the construction of a "cost model". We present experimental results that demonstrate that the model can be used to predict the effect of certain changes to the case memory. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103528. 	[Title] ADAPtER: an Integrated Diagnostic System Combining Case-Based and Abductive Reasoning [Abstract] The aim of this paper is to describe the ADAPtER system, a diagnostic architecture combining case-based reasoning with abductive reasoning and exploiting the adaptation of the solution of old episodes, in order to focus the reasoning process. Domain knowledge is represented via a logical model and basic mechanisms, based on abductive reasoning with consistency constraints, have been defined for solving complex diagnostic problems involving multiple faults. The model-based component has been supplemented with a case memory and adaptation mechanisms have been developed, in order to make the diagnostic system able to exploit past experience in solving new cases. A heuristic function is proposed, able to rank the solutions associated to retrieved cases with respect to the adaptation effort needed to transform such solutions into possible solutions for the current case. We will discuss some preliminary experiments showing the validity of the above heuristic and the convenience of solving a new case by adapting a retrieved solution rather than solving the new problem from scratch. 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 #101143. 	[Title] Issues in Goal-Driven Explanation [Abstract] When a reasoner explains surprising events for its internal use, a key motivation for explaining is to perform learning that will facilitate the achievement of its goals. Human explainers use a range of strategies to build explanations, including both internal reasoning and external information search, and goal-based considerations have a profound effect on their choices of when and how to pursue explanations. However, standard AI models of explanation rely on goal-neutral use of a single fixed strategy|generally backwards chaining|to build their explanations. This paper argues that explanation should be modeled as a goal-driven learning process for gathering and transforming information, and discusses the issues involved in developing an active multi-strategy process for goal-driven explanation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #101145. 	[Title] Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation* [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #111676. 	[Title] Goal-based Explanation Evaluation 1 [Abstract] 1 I would like to thank my dissertation advisor, Roger Schank, for his very valuable guidance on this research, and to thank the Cognitive Science reviewers for their helpful comments on a draft of this paper. The research described here was conducted primarily at Yale University, supported in part by the Defense Advanced Research Projects Agency, monitored by the Office of Naval Research under contract N0014-85-K-0108 and by the Air Force Office of Scientific Research under contract F49620-88-C-0058. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Paper #89308 has the title `Focusing Construction and Selection of Abductive Hypotheses  
` and the abstract `Many abductive understanding systems explain novel situations by a chaining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive hypotheses are focused by specific explanations of prior episodes and by goal-based criteria reflecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday domains. We identify five central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in domains that are complex and imperfectly un derstood.
`.
Question: Which category should Paper #89308 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #89308 has the title `Focusing Construction and Selection of Abductive Hypotheses  
` and the abstract `Many abductive understanding systems explain novel situations by a chaining process that is neutral to explainer needs beyond generating some plausible explanation for the event being explained. This paper examines the relationship of standard models of abductive understanding to the case-based explanation model. In case-based explanation, construction and selection of abductive hypotheses are focused by specific explanations of prior episodes and by goal-based criteria reflecting current information needs. The case-based method is inspired by observations of human explanation of anomalous events during everyday understanding, and this paper focuses on the method's contributions to the problems of building good explanations in everyday domains. We identify five central issues, compare how those issues are addressed in traditional and case-based explanation models, and discuss motivations for using the case-based approach to facilitate generation of plausible and useful explanations in domains that are complex and imperfectly un derstood.
`.
Question: Which category should Paper #89308 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