Above are papers related to the following paper: [Paper_ID] Paper #55770. 	[Title] The case for cases: a call for purity in case-based reasoning inherently more difficult than [Abstract] A basic premise of case-based reasoning (CBR) is that it involves reasoning from cases, which are representations of real episodes, rather than from rules, which are facts and if then structures with no stated connection to any real episodes. In fact, most CBR systems do not reason directly from cases | rather they reason from abstractions or simplifications of cases. In this paper, we argue for "pure" case-based reasoning, i.e., reasoning from representations that are both concrete and reasonably complete. We claim that working from representations that satisfy these criteria We illustrate our argument with examples from three previous systems, chef, swale, and hypo, as well as from cookie, a CBR system being developed by the first author. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20193. 	[Title] The Structure-Mapping Engine: Algorithm and Examples [Abstract] This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact forbus@ils.nwu.edu Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112099. 	[Title] Massively Parallel Matching of Knowledge Structures [Abstract] As knowledge bases used for AI systems increase in size, access to relevant information is the dominant factor in the cost of inference. This is especially true for analogical (or case-based) reasoning, in which the ability of the system to perform inference is dependent on efficient and flexible access to a large base of exemplars (cases) judged likely to be relevant to solving a problem at hand. In this chapter we discuss a novel algorithm for efficient associative matching of relational structures in large semantic networks. The structure matching algorithm uses massively parallel hardware to search memory for knowledge structures matching a given probe structure. The algorithm is built on top of PARKA, a massively parallel knowledge representation system which runs on the Connection Machine. We are currently exploring the utility of this algorithm in CaPER, a case-based planning system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10435. 	[Title] Improving accuracy by combining rule-based and case-based reasoning [Abstract] An architecture is presented for combining rule-based and case-based reasoning. The architecture is intended for domains that are understood reasonably well, but still imperfectly. It uses a set of rules, which are taken to be only approximately correct, to obtain a preliminary answer for a given problem; it then draws analogies from cases to handle exceptions to the rules. Having rules together with cases not only increases the architecture's domain coverage, it also allows innovative ways of doing case-based reasoning: the same rules that are used for rule-based reasoning are also used by the case-based component to do case indexing and case adaptation. The architecture was applied to the task of name pronunciation, and, with minimal knowledge engineering, was found to perform almost at the level of the best commercial systems. Moreover, its accuracy was found to exceed what it could have achieved with rules or cases alone, thus demonstrating the accuracy improvement afforded by combining rule-based and case-based reasoning. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5062. 	[Title] A Memory Model for Case Retrieval by Activation Passing [Abstract]  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 #55801. 	[Title] Within the Letter of the Law: open-textured planning [Abstract] Most case-based reasoning systems have used a single "best" or "most similar" case as the basis for a solution. For many problems, however, there is no single exact solution. Rather, there is a range of acceptable answers. We use cases not only as a basis for a solution, but also to indicate the boundaries within which a solution can be found. We solve problems by choosing some point within those boundaries. In this paper, I discuss this use of cases with illustrations from chiron, a system I have implemented in the domain of personal income tax planning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #67633. 	[Title] Massively Parallel Support for Case-based Planning [Abstract] In case-based planning (CBP), previously generated plans are stored as cases in memory and can be reused to solve similar planning problems in the future. CBP can save considerable time over planning from scratch (generative planning), thus offering a potential (heuristic) mechanism for handling intractable problems. One drawback of CBP systems has been the need for a highly structured memory that requires significant domain engineering and complex memory indexing schemes to enable efficient case retrieval. In contrast, our CBP system, CaPER, is based on a massively parallel frame-based AI language and can do extremely fast retrieval of complex cases from a large, unindexed memory. The ability to do fast, frequent retrievals has many advantages: indexing is unnecessary; very large casebases can be used; and memory can be probed in numerous alternate ways, allowing more specific retrieval of stored plans that better fit a target problem with less adaptation. fl Preliminary version of an article appearing in IEEE Expert, February 1994, pp. 8-14. This paper is an extended version of [1]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114777. 	[Title] The Case for Graph-Structured Representations [Abstract] Case-based reasoning involves reasoning from cases: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term "graph-structured" for representations that (1) are capable of expressing the relations between any two objects in a case, (2) allow the set of relations used to vary from case to case, and (3) allow the set of possible relations to be expanded as necessary to describe new cases. Such representations can be implemented as, for example, semantic networks or lists of concrete propositions in some logic. We believe that graph-structured representations offer significant advantages, and thus we are investigating ways to implement such representations efficiently. We make a "case-based argument" using examples from two systems, chiron and caper, to show how a graph-structured representation supports two different kinds of case-based planning in two different domains. We discuss the costs associated with graph-structured representations and describe an approach to reducing those costs, imple mented in caper. 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 #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.

Above are papers related to the following paper: [Paper_ID] Paper #208345. 	[Title] CHIRON: Planning in an Open-Textured Domain [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20924. 	[Title] Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics [Abstract] We propose a probabilistic case-space metric for the case matching and case adaptation tasks. Central to our approach is a probability propagation algorithm adopted from Bayesian reasoning systems, which allows our case-based reasoning system to perform theoretically sound probabilistic reasoning. The same probability propagation mechanism actually offers a uniform solution to both the case matching and case adaptation problems. We also show how the algorithm can be implemented as a connectionist network, where efficient massively parallel case retrieval is an inherent property of the system. We argue that using this kind of an approach, the difficult problem of case indexing can be completely avoided. Pp. 144-154 in Topics in Case-Based Reasoning, edited by Stefan Wess, Klaus-Dieter Althoff and Michael M. Richter. Volume 837, Lecture Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521183. 	[Title] Formalising the knowledge content of case memory systems [Abstract] Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this `knowledge content' which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumlates problem-solving experience. The current paper presents a `case-base semantics' which generalises recent formalisations of case-based classification. Within this framework, the paper explores various issues in assuring that these sematics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the case-base. Please summarise all the given information.

Paper #55770 has the title `The case for cases: a call for purity in case-based reasoning inherently more difficult than
` and the abstract `A basic premise of case-based reasoning (CBR) is that it involves reasoning from cases, which are representations of real episodes, rather than from rules, which are facts and if then structures with no stated connection to any real episodes. In fact, most CBR systems do not reason directly from cases | rather they reason from abstractions or simplifications of cases. In this paper, we argue for "pure" case-based reasoning, i.e., reasoning from representations that are both concrete and reasonably complete. We claim that working from representations that satisfy these criteria We illustrate our argument with examples from three previous systems, chef, swale, and hypo, as well as from cookie, a CBR system being developed by the first author.
`.
Question: Which category should Paper #55770 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #55770 has the title `The case for cases: a call for purity in case-based reasoning inherently more difficult than
` and the abstract `A basic premise of case-based reasoning (CBR) is that it involves reasoning from cases, which are representations of real episodes, rather than from rules, which are facts and if then structures with no stated connection to any real episodes. In fact, most CBR systems do not reason directly from cases | rather they reason from abstractions or simplifications of cases. In this paper, we argue for "pure" case-based reasoning, i.e., reasoning from representations that are both concrete and reasonably complete. We claim that working from representations that satisfy these criteria We illustrate our argument with examples from three previous systems, chef, swale, and hypo, as well as from cookie, a CBR system being developed by the first author.
`.
Question: Which category should Paper #55770 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