Above are papers related to the following paper: [Paper_ID] Paper #91852. 	[Title] Model-Based Learning of Structural Indices to Design Cases [Abstract] A major issue in case-basedsystems is retrieving the appropriate cases from memory to solve a given problem. This implies that a case should be indexed appropriately when stored in memory. A case-based system, being dynamic in that it stores cases for reuse, needs to learn indices for the new knowledge as the system designers cannot envision that knowledge. Irrespective of the type of indexing (structural or functional), a hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary and learning the right level of generalization. In this paper we show how structure-behavior-function (SBF) models help in learning structural indices to design cases in the domain of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design provides both the vocabulary for structural indexing of design cases and the inductive biases for index generalization. We further discuss how model-based learning can be integrated with similarity-based learning (that uses prior design cases) for learning the level of index generalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152896. 	[Title] Abstract [Abstract] Self-selection of input examples on the basis of performance failure is a powerful bias for learning systems. The definition of what constitutes a learning bias, however, has been typically restricted to bias provided by the input language, hypothesis language, and preference criteria between competing concept hypotheses. But if bias is taken in the broader context as any basis that provides a preference for one concept change over another, then the paradigm of failure-driven processing indeed provides a bias. Bias is exhibited by the selection of examples from an input stream that are examples of failure; successful performance is filtered out. We show that the degrees of freedom are less in failure-driven learning than in success-driven learning and that learning is facilitated because of this constraint. We also broaden the definition of failure, provide a novel taxonomy of failure causes, and illustrate the interaction of both in a multistrategy learning system called Meta-AQUA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35490. 	[Title] A Model-Based Approach to Blame-Assignment in Design [Abstract] We analyze the blame-assignment task in the context of experience-based design and redesign of physical devices. We identify three types of blame-assignment tasks that differ in the types of information they take as input: the design does not achieve a desired behavior of the device, the design results in an undesirable behavior, a specific structural element in the design misbehaves. We then describe a model-based approach for solving the blame-assignment task. This approach uses structure-behavior-function models that capture a designer's comprehension of the way a device works in terms of causal explanations of how its structure results in its behaviors. We also address the issue of indexing the models in memory. We discuss how the three types of blame-assignment tasks require different types of indices for accessing the models. Finally we describe the KRITIK2 system that implements and evaluates this model-based approach to blame assignment. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28265. 	[Title] Using Introspective Reasoning to Select Learning Strategies [Abstract] In order to learn effectively, a system must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge it needs to improve its performance at the current task. Introspection requires a declaratflive representation of the reasoning performed by the system during the performance task. This paper presents a taxonomy of possible reasoning failures that can occur during this task, their declarative representations, and their associations with particular learning strategies. We propose a theory of Meta-XPs, which are explanation structures that help the system identify failure types and choose appropriate learning strategies in order to avoid similar mistakes in the future. A program called Meta-AQUA embodies the theory and processes examples in the domain of drug smuggling. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #91853. 	[Title] GIT-CC-92/60 A Model-Based Approach to Analogical Reasoning and Learning in Design [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #38829. 	[Title] Learning Generic Mechanisms from Experiences for Analogical Reasoning [Abstract] Humans appear to often solve problems in a new domain by transferring their expertise from a more familiar domain. However, making such cross-domain analogies is hard and often requires abstractions common to the source and target domains. Recent work in case-based design suggests that generic mechanisms are one type of abstractions used by designers. However, one important yet unexplored issue is where these generic mechanisms come from. We hypothesize that they are acquired incrementally from problem-solving experiences in familiar domains by generalization over patterns of regularity. Three important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. In this paper, we show that mental models in a familiar domain provide the content, and together with the problem-solving context in which learning occurs, also provide the constraints for learning generic mechanisms from design experiences. In particular, we show how the model-based learning method integrated with similarity-based learning addresses the issues in generalization from experiences. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #943. 	[Title] Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases [Abstract] This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the process of using past experiences stored in the reasoner's memory to understand novel situations or solve novel problems. However, this process assumes that past experiences are well understood and provide good "lessons" to be used for future situations. This assumption is usually false when one is learning about a novel domain, since situations encountered previously in this domain might not have been understood completely. Furthermore, the reasoner may not even have a case that adequately deals with the new situation, or may not be able to access the case using existing indices. We present a theory of incremental learning based on the revision of previously existing case knowledge in response to experiences in such situations. The theory has been implemented in a case-based story understanding program that can (a) learn a new case in situations where no case already exists, (b) learn how to index the case in memory, and (c) incrementally refine its understanding of the case by using it to reason about new situations, thus evolving a better understanding of its domain through experience. This research complements work in case-based reasoning by providing mechanisms by which a case library can be automatically built for use by a case-based reasoning program. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #117328. 	[Title] Functional Representation as Design Rationale [Abstract] Design rationale is a record of design activity: of alternatives available, choices made, the reasons for them, and explanations of how a proposed design is intended to work. We describe a representation called the Functional Representation (FR) that has been used to represent how a device's functions arise causally from the functions of its components and their interconnections. We propose that FR can provide the basis for capturing the causal aspects of the design rationale. We briefly discuss the use of FR for a number of tasks in which we would expect the design rationale to be useful: generation of diagnostic knowledge, design verification and redesign. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73327. 	[Title] Modeling Case-based Planning for Repairing Reasoning Failures [Abstract] One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures of its own reasoning process. We address the issues of the transferability of such models versus the specificity of the knowledge in them, the kinds of knowledge needed for self-modeling and how that knowledge is structured, and the evaluation of introspective reasoning systems. We present the ROBBIE system which implements a model of its planning processes to improve the planner in response to reasoning failures. We show how ROBBIE's hierarchical model balances model generality with access to implementation-specific details, and discuss the qualitative and quantitative measures we have used for evaluating its introspective component. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20178. 	[Title] Discovery of Physical Principles from Design Experiences [Abstract] One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represent domain principles as device-independent behavior-function (BF) models. We show that (i) the function of a device determines what to generalize from its SBF model, (ii) the SBF model itself suggests how far to generalize, and (iii) the typology of functions indicates what method to use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #64271. 	[Title] Generic Teleological Mechanisms and their Use in Case Adaptation [Abstract] In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types of knowledge and reasoning useful for different classes of case-adaptation tasks. In this paper, we examine a class of non-routine case-adaptation tasks that involve patterned insertions of new elements in old solutions. We describe a model-based method for solving this task in the context of the design of physical devices. The method uses knowledge of generic teleological mechanisms (GTMs) such as cascading. Old designs are adapted to meet new functional specifications by accessing and instantiating the appropriate GTM. The Kritik2 system evaluates the computational feasibility and sufficiency of this method for design adaptation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20180. 	[Title] Learning Indices for Schema Selection [Abstract] In addition to learning new knowledge, a system must be able to learn when the knowledge is likely to be applicable. An index is a piece of information which, when identified in a given situation, triggers the relevant piece of knowledge (or schema) in the system's memory. We discuss the issue of how indices may be learned automatically in the context of a story understanding task, and present a program that can learn new indices for existing explanatory schemas. We discuss two methods using which the system can identify the relevant schema even if the input does not directly match an existing index, and learn a new index to allow it to retrieve this schema more efficiently in the future. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3828. 	[Title] A theory of questions and question asking [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152244. 	[Title] The Role of Generic Models in Conceptual Change [Abstract] 1 This research was funded in part by NSF Grant No. IRI-92-10925 and in part by ONR Grant No. N00014-92-J-1234. We thank John Clement for the use of his protocol transcript, James Greeno for his contribution to developing our constructive modeling interpretation of it, and Ryan Tweney for his helpful comments Todd W. Griffith, Nancy J. Nersessian, and Ashok Goel Abstract We hypothesize generic models to be central in conceptual change in science. This hypothesis has its origins in two theoretical sources. The first source, constructive modeling, derives from a philosophical theory that synthesizes analyses of historical conceptual changes in science with investigations of reasoning and representation in cognitive psychology. The theory of constructive modeling posits generic mental models as productive in conceptual change. The second source, adaptive modeling, derives from a computational theory of creative design. Both theories posit situation independent domain abstractions, i.e. generic models. Using a constructive modeling interpretation of the reasoning exhibited in protocols collected by John Clement (1989) of a problem solving session involving conceptual change, we employ the resources of the theory of adaptive modeling to develop a new computational model, ToRQUE. Here we describe a piece of our analysis of the protocol to illustrate how our synthesis of the two theories is being used to develop a system for articulating and testing ToRQUE. The results of our research show how generic modeling plays a central role in conceptual change. They also demonstrate how such an interdisciplinary synthesis can provide significant insights into scientific reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #95188. 	[Title] KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM [Abstract] In the late 1980s, we developed one of the early case-based design systems called Kritik. Kritik autonomously generated preliminary (conceptual, qualitative) designs for physical devices by retrieving and adapting past designs stored in its case memory. Each case in the system had an associated structure-behavior-function (SBF) device model that explained how the structure of the device accomplished its functions. These casespecific device models guided the process of modifying a past design to meet the functional specification of a new design problem. The device models also enabled verification of the design modifications. Kritik2 is a new and more complete implementation of Kritik. In this paper, we take a retrospective view on Kritik. In early papers, we had described Kritik as integrating case-based and model-based reasoning. In this integration, Kritik also grounds the computational process of case-based reasoning in the SBF content theory of device comprehension. The SBF models not only provide methods for many specific tasks in case-based design such as design adaptation and verification, but they also provide the vocabulary for the whole process of case-based design, from retrieval of old cases to storage of new ones. This grounding, we believe, is essential for building well-constrained theories of case-based design. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20179. 	[Title] Use of Mental Models for Constraining Index Learning in Experience-Based Design [Abstract] The power of the case-based method comes from the ability to retrieve the "right" case when a new problem is specified. This implies that learning the "right" indices to a case before storing it for potential reuse is crucial for the success of the method. A hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary, and learning the right level of generalization. In this paper we show how the use of structure-behavior-function (SBF) models constrains index learning in the context of experience-based design of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design, together with a specification of the task for which the design case might be reused, provides the vocabulary for indexing the design case in memory. We also discuss how the prior design experiences stored in case-memory help to determine the level of index generalization. The KRITIK2 system implements and evaluates the model-based method for learning indices to design cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115166. 	[Title] EXPLANATORY INTERFACE IN INTERACTIVE DESIGN ENVIRONMENTS [Abstract] Explanation is an important issue in building computer-based interactive design environments in which a human designer and a knowledge system may cooperatively solve a design problem. We consider the two related problems of explaining the system's reasoning and the design generated by the system. In particular, we analyze the content of explanations of design reasoning and design solutions in the domain of physical devices. We describe two complementary languages: task-method-knowledge models for explaining design reasoning, and structure-behavior-function models for explaining device designs. INTERACTIVE KRITIK is a computer program that uses these representations to visually illustrate the system's reasoning and the result of a design episode. The explanation of design reasoning in INTERACTIVE KRITIK is in the context of the evolving design solution, and, similarly, the explanation of the design solution is in the context of the design reasoning. Please summarise all the given information.

Paper #91852 has the title `Model-Based Learning of Structural Indices to Design Cases  
` and the abstract `A major issue in case-basedsystems is retrieving the appropriate cases from memory to solve a given problem. This implies that a case should be indexed appropriately when stored in memory. A case-based system, being dynamic in that it stores cases for reuse, needs to learn indices for the new knowledge as the system designers cannot envision that knowledge. Irrespective of the type of indexing (structural or functional), a hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary and learning the right level of generalization. In this paper we show how structure-behavior-function (SBF) models help in learning structural indices to design cases in the domain of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design provides both the vocabulary for structural indexing of design cases and the inductive biases for index generalization. We further discuss how model-based learning can be integrated with similarity-based learning (that uses prior design cases) for learning the level of index generalization. 
`.
Question: Which category should Paper #91852 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #91852 has the title `Model-Based Learning of Structural Indices to Design Cases  
` and the abstract `A major issue in case-basedsystems is retrieving the appropriate cases from memory to solve a given problem. This implies that a case should be indexed appropriately when stored in memory. A case-based system, being dynamic in that it stores cases for reuse, needs to learn indices for the new knowledge as the system designers cannot envision that knowledge. Irrespective of the type of indexing (structural or functional), a hierarchical organization of the case memory raises two distinct but related issues in index learning: learning the indexing vocabulary and learning the right level of generalization. In this paper we show how structure-behavior-function (SBF) models help in learning structural indices to design cases in the domain of physical devices. The SBF model of a design provides the functional and causal explanation of how the structure of the design delivers its function. We describe how the SBF model of a design provides both the vocabulary for structural indexing of design cases and the inductive biases for index generalization. We further discuss how model-based learning can be integrated with similarity-based learning (that uses prior design cases) for learning the level of index generalization. 
`.
Question: Which category should Paper #91852 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