Above are papers related to the following paper: [Paper_ID] Paper #1117184. 	[Title] NACODAE: Navy Conversational Decision Aids Environment [Abstract] This report documents NACODAE, the Navy Conversational Decision Aids Environment being developed at the Navy Center for Applied Research in Artificial Intelligence (NCARAI), which is a branch of the Naval Research Laboratory. NA-CODAE is a software prototype that is being developed under the Practical Advances in Case-Based Reasoning project, which is funded by the Office for Naval Research, for the purpose of assisting Navy and other DoD personnel in decision aids tasks such as system maintenance, operational training, crisis response planning, logistics, fault diagnosis, target classification, and meteorological nowcasting. Implemented in Java, NACODAE can be used on any machine containing a Java virtual machine (e.g., PCs, Unix). This document describes and exemplifies NACODAE's capabilities. Our goal is to transition this tool to operational personnel, and to continue its enhancement through user feedback and by testing recent research advances in case-based reasoning and related areas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106052. 	[Title] Lazy Acquisition of Place Knowledge [Abstract] In this paper we define the task of place learning and describe one approach to this problem. Our framework represents distinct places as evidence grids, a probabilistic description of occupancy. Place recognition relies on nearest neighbor classification, augmented by a registration process to correct for translational differences between the two grids. The learning mechanism is lazy in that it involves the simple storage of inferred evidence grids. Experimental studies with physical and simulated robots suggest that this approach improves place recognition with experience, that it can handle significant sensor noise, that it benefits from improved quality in stored cases, and that it scales well to environments with many distinct places. Additional studies suggest that using historical information about the robot's path through the environment can actually reduce recognition accuracy. Previous researchers have studied evidence grids and place learning, but they have not combined these two powerful concepts, nor have they used systematic experimentation to evaluate their methods' abilities. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17798. 	[Title] Using Decision Trees to Improve Case-Based Learning [Abstract] This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #13960. 	[Title] Using a Case Base of Surfaces to Speed-Up Reinforcement Learning [Abstract] This paper demonstrates the exploitation of certain vision processing techniques to index into a case base of surfaces. The surfaces are the result of reinforcement learning and represent the optimum choice of actions to achieve some goal from anywhere in the state space. This paper shows how strong features that occur in the interaction of the system with its environment can be detected early in the learning process. Such features allow the system to identify when an identical, or very similar, task has been solved previously and to retrieve the relevant surface. This results in an orders of magnitude increase in learning rate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4878. 	[Title] Cooperative Case-Based Reasoning [Abstract] We are investigating possible modes of cooperation among homogeneous agents with learning capabilities. In this paper we will be focused on agents that learn and solve problems using Case-based Reasoning (CBR), and we will present two modes of cooperation among them: Distributed Case-based Reasoning (DistCBR) and Collective Case-based Reasoning (ColCBR). We illustrate these modes with an application where different CBR agents able to recommend chromatography techniques for protein purification cooperate. The approach taken is to extend Noos, the representation language being used by the CBR agents. Noos is knowledge modeling framework designed to integrate learning methods and based on the task/method decomposition principle. The extension we present, Plural Noos, allows communication and cooperation among agents implemented in Noos by means of three basic constructs: alien references, foreign method evaluation, and mobile methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #359067. 	[Title] Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning [Abstract] Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integrations with other reasoning approaches. This paper briefly describes our group's ongoing efforts towards enhancing the inferencing behaviors of a conversational case-based reasoning development tool named NaCoDAE. In particular, we focus on integrating NaCoDAE with machine learning, model-based reasoning, and generative planning modules. This paper defines CCBR, briefly summarizes the integrations, and explains how they enhance the overall system. Our research focuses on enhancing the performance of conversational case-based reasoning (CCBR) systems (Aha & Breslow, 1997). CCBR is a form of case-based reasoning where users initiate problem solving conversations by entering an initial problem description in natural language text. This text is assumed to be a partial rather than a complete problem description. The CCBR system then assists in eliciting refinements of this description and in suggesting solutions. Its primary purpose is to provide a focus of attention for the user so as to quickly provide a solution(s) for their problem. Figure 1 summarizes the CCBR problem solving cycle. Cases in a CCBR library have three components: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #200480. 	[Title] CBET: a Case Base Exploration Tool [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108267. 	[Title] Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks [Abstract] We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and this paper concentrates on simulated soccer as an example. Bayesian networks are used to characterize action selection whereas a case-based approach is used to determine how to implement actions. This paper has two contributions. First, we survey integrations of case-based and Bayesian approaches from the perspective of a popular CBR task decomposition framework, thus explaining what types of integrations have been attempted. This allows us to explain the unique aspects of our proposed integration. Second, we demonstrate how Bayesian nets can be used to provide environmental context, and thus feature selection information, for the case-based reasoner. 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 #1120563. 	[Title] Preparing Case Retrieval Nets for Distributed Processing [Abstract] In this paper, we discuss two approaches of applying the memory model of Case Retrieval Nets to applications where a distributed processing of information is required. For this, we distinguish two types of such applications, namely (a) the case of distributed case libraries and (b) the case of distributed cases. While a solution to the former is straightforward, the latter requires an extension to Case Retrieval Nets which provides a kind of partitioning of the entire net structure. This extended model even allows for a concurrent implementation of the retrieval process or for the use of collaborative agents for retrieval. Keywords: Case-based reasoning, case retrieval, memory structures, distributed processing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152308. 	[Title] Learning to Refine Case Libraries: [Abstract] Initial Results Abstract. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e., precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521269. 	[Title] Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures [Abstract] In [9] we introduced a formal framework for constructing ordinal similarity measures, and suggested how this might also be applied to cardinal measures. In this paper we will place this approach in a more general framework, called similarity metrics. In this framework, ordinal similarity metrics (where comparison returns a boolean value) can be combined with cardinal metrics (returning a numeric value) and, indeed, with metrics returning values of other types, to produce new metrics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #53942. 	[Title] Simplifying Decision Trees: A Survey [Abstract] Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111614. 	[Title] SaxEx a case-based reasoning system for generating expressive musical performances [Abstract] We have studied the problem of generating expressive musical performances in the context of tenor saxophone interpretations. We have done several recordings of a tenor sax playing different Jazz ballads with different degrees of expressiveness including an inexpressive interpretation of each ballad. These recordings are analyzed, using SMS spectral modeling techniques, to extract information related to several expressive parameters. This set of parameters and the scores constitute the set of cases (examples) of a case-based system. From this set of cases, the system infers a set of possible expressive transformations for a given new phrase applying similarity criteria, based on background musical knowledge, between this new phrase and the set of cases. Finally, SaxEx applies the inferred expressive transformations to the new phrase using the synthesis capabilities of SMS. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119295. 	[Title] Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on [Abstract] This paper tackles the supervised induction of a distance from examples described as Horn clauses or constrained clauses. In opposition to syntax-driven approaches, this approach is discrimination-driven: it proceeds by defining a small set of complex discriminant hypotheses. These hypotheses serve as new concepts, used to redescribe the initial examples. Further, this redescription can be embedded into the space of natural integers, and a distance between examples thus naturally follows. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #239810. 	[Title] A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner [Abstract] Conversational case-based reasoning (CCBR) is a form of interactive case-based reasoning where users input a partial problem description (in text). The CCBR system responds with a ranked solution display, which lists the solutions of stored cases whose problem descriptions best match the user's, and a ranked question display, which lists the unanswered questions in these cases. Users interact with these displays, either refining their problem description by answering selected questions, or selecting a solution to apply. CCBR systems should support dialogue inferencing; they should infer answers to questions that are implied by the problem description. Otherwise, questions will be listed that the user believes they have already answered. The standard approach to dialogue inferencing allows case library designers to insert rules that define implications between the problem description and unanswered questions. However, this approach imposes substantial knowledge engineering requirements. We introduce an alternative approach whereby an intelligent assistant guides the designer in defining a model of their case library, from which implication rules are derived. We detail this approach, its benefits, and explain how it can be supported through an integration with Parka-DB, a fast relational database system. We will evaluate our approach in the context of our CCBR system, named NaCoDAE. This paper appeared at the 1998 AAAI Spring Symposium on Multimodal Reasoning, and is NCARAI TR AIC-97-023. We introduce an integrated reasoning approach in which a model-based reasoning component performs an important inferencing role in a conversational case-based reasoning (CCBR) system named NaCoDAE (Breslow & Aha, 1997) (Figure 1). CCBR is a form of case-based reasoning where users enter text queries describing a problem and the system assists in eliciting refinements of it (Aha & Breslow, 1997). Cases have three components: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14531. 	[Title] Context-Sensitive Feature Selection for Lazy Learners [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #399173. 	[Title] Corporate Memories as Distributed Case Libraries [Abstract] Rising operating costs and structural transformations such as resizing and globaliza-tion of companies all over the world have brought into focus the emerging discipline of knowledge management that is concerned with making knowledge pay off. Corporate memories form an important part of such knowledge management initiatives in a company. In this paper, we discuss how viewing corporate memories as distributed case libraries can benefit from existing techniques for distributed case-based reasoning for resource discovery and exploitation of previous expertise. We present two techniques developed in the context of multi-agent case-based reasoning for accessing and exploiting past experience from corporate memory resources. The first approach, called Negotiated Retrieval, deals with retrieving and assembling "case pieces" from different resources in a corporate memory to form a good overall case. The second approach, based on Federated Peer Learning, deals with two modes of cooperation called DistCBR and ColCBR that let an agent exploit the experience and expertise of peer agents to achieve a local task. fl The first author would like to acknowledge the support by the National Science Foundation under Grant Nos. IRI-9523419 and EEC-9209623. The second author's research reported in this paper has been developed at the IIIA inside the ANALOG Project funded by Spanish CICYT grant 122/93. The content of this paper does not necessarily reflect the position or the policy of the US Government, the Kingdom of Spain Government, or the Catalonia Government, and no official endorsement should be inferred. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1481. 	[Title] (1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological [Abstract] Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119178. 	[Title] Correcting for Length Biasing in Conversational Case Scoring [Abstract] Inference's conversational case-based reasoning (CCBR) approach, embedded in the CBR Content Navigator line of products, is susceptible to a bias in its case scoring algorithm. In particular, shorter cases tend to be given higher scores, assuming all other factors are held constant. This report summarizes our investigation for mediating this bias. We introduce an approach for eliminating this bias and evaluate how it affects retrieval performance for six case libraries. We also suggest explanations for these results, and note the limitations of our study. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113934. 	[Title] Integration of Case-Based Reasoning and Neural Networks Approaches for Classification [Abstract] Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1 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 #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 #1102567. 	[Title] Computer-Supported Argumentation for Cooperative Design on the World-Wide Web [Abstract] This paper describes an argumentation system for cooperative design applications on the Web. The system provides experts involved in such procedures means of expressing and weighing their individual arguments and preferences, in order to argue for or against the selection of a certain choice. It supports defeasible and qualitative reasoning in the presence of ill-structured information. Argumentation is performed through a set of discourse acts which call a variety of procedures for the propagation of information in the corresponding discussion graph. The paper also reports on the integration of Case Based Reasoning techniques, used to resolve current design issues by considering previous similar situations, and the specitcation of similarity measures between the various argumentation items, the aim being to estimate the variations among opinions of the designers involved in cooperative design. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56167. 	[Title] Refining Conversational Case Libraries [Abstract] Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rather than more problematic rules, and they can be incrementally extended. However, rather than eliminate the knowledge engineering bottleneck, they refocus it on case engineering, the task of carefully authoring cases according to library design guidelines to ensure good performance. Designing complex libraries according to these guidelines is difficult; software is needed to assist users with case authoring. We describe an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114864. 	[Title] Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1) [Abstract] This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in my WWW pages under slides. Please contact me if you have any questions. Please also note the date (listed above) on which this was most recently updated. While I plan to make occasional updates to this file, it is bound to be outdated quickly. Also, I apologize for the lack of figures, but my time on this project is limited and the slides should compensate. Finally, this bibliography is, by definition, This book is now out of date. Both Pat Langley and Tom Mitchell are in the process of writing textbooks on this subject, but we're still waiting for them. Until then, I suggest looking at both the Readings and the recent ML conference proceedings (both International and European). There are also a few introductory papers on this subject, though I haven't gotten around to putting them in here yet. However, Pat Langley and Dennis Kibler (1988) have written a good paper on ML as an empirical science, and Pat has written several editorials of use to the ML author (Langley 1986; 1987; 1990). incomplete, and I've left out many other references that may be of some use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521207. 	[Title] A Formal Analysis of Case Base Retrieval [Abstract] Case based systems typically retrieve cases from the case base by applying similarity measures. The measures are usually constructed in an ad hoc manner. This report presents a toolbox for the systematic construction of similarity measures. In addition to paving the way to a design methodology for similarity measures, this systematic approach facilitates the identification of opportunities for parallelisation in case base retrieval. Please summarise all the given information.

Paper #1117184 has the title `NACODAE: Navy Conversational Decision Aids Environment  
` and the abstract `This report documents NACODAE, the Navy Conversational Decision Aids Environment being developed at the Navy Center for Applied Research in Artificial Intelligence (NCARAI), which is a branch of the Naval Research Laboratory. NA-CODAE is a software prototype that is being developed under the Practical Advances in Case-Based Reasoning project, which is funded by the Office for Naval Research, for the purpose of assisting Navy and other DoD personnel in decision aids tasks such as system maintenance, operational training, crisis response planning, logistics, fault diagnosis, target classification, and meteorological nowcasting. Implemented in Java, NACODAE can be used on any machine containing a Java virtual machine (e.g., PCs, Unix). This document describes and exemplifies NACODAE's capabilities. Our goal is to transition this tool to operational personnel, and to continue its enhancement through user feedback and by testing recent research advances in case-based reasoning and related areas. 
`.
Question: Which category should Paper #1117184 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1117184 has the title `NACODAE: Navy Conversational Decision Aids Environment  
` and the abstract `This report documents NACODAE, the Navy Conversational Decision Aids Environment being developed at the Navy Center for Applied Research in Artificial Intelligence (NCARAI), which is a branch of the Naval Research Laboratory. NA-CODAE is a software prototype that is being developed under the Practical Advances in Case-Based Reasoning project, which is funded by the Office for Naval Research, for the purpose of assisting Navy and other DoD personnel in decision aids tasks such as system maintenance, operational training, crisis response planning, logistics, fault diagnosis, target classification, and meteorological nowcasting. Implemented in Java, NACODAE can be used on any machine containing a Java virtual machine (e.g., PCs, Unix). This document describes and exemplifies NACODAE's capabilities. Our goal is to transition this tool to operational personnel, and to continue its enhancement through user feedback and by testing recent research advances in case-based reasoning and related areas. 
`.
Question: Which category should Paper #1117184 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