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 #566664. 	[Title] Making SME greedy and pragmatic [Abstract] The Structure-Mapping Engine (SME) has successfully modeled several aspects of human consistent interpretations of an analogy. While useful for theoretical explorations, this aspect of the algorithm is both psychologically implausible and computationally inefficient. (2) SME contains no mechanism for focusing on interpretations relevant to an analogizer's goals. This paper describes modifications to SME which overcome these flaws. We describe a greedy merge algorithm which efficiently computes an approximate "best" interpretation, and can generate alternate interpretations when necessary. We describe pragmatic marking, a technique which focuses the mapping to produce relevant, yet novel, inferences. We illustrate these techniques via example and evaluate their performance using empirical data and theoretical analysis. analogical processing. However, it has two significant drawbacks: (1) SME constructs all structurally Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3085. 	[Title] MAC/FAC: A Model of Similarity-based Retrieval [Abstract] We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than structural remindings. (3) People sometimes experience and use purely structural analogical re-mindings. Our model, called MAC/FAC (for "many are called but few are chosen") consists of two stages. The first stage (MAC) uses a computationally cheap, non-structural matcher to filter candidates from a pool of memory items. That is, we redundantly encode structured representations as content vectors, whose dot product yields an estimate of how well the corresponding structural representations will match. The second stage (FAC) uses SME to compute a true structural match between the probe and output from the first stage. MAC/FAC has been fully implemented, and we show that it is capable of modeling patterns of access found in psychological data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15889. 	[Title] Structural Similarity as Guidance in Case-Based Design [Abstract] This paper presents a novel approach to determine structural similarity as guidance for adaptation in case-based reasoning (Cbr). We advance structural similarity assessment which provides not only a single numeric value but the most specific structure two cases have in common, inclusive of the modification rules needed to obtain this structure from the two cases. Our approach treats retrieval, matching and adaptation as a group of dependent processes. This guarantees the retrieval and matching of not only similar but adaptable cases. Both together enlarge the overall problem solving performance of Cbr and the explainability of case selection and adaptation considerably. Although our approach is more theoretical in nature and not restricted to a specific domain, we will give an example taken from the domain of industrial building design. Additionally, we will sketch two prototypical implementations of this approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129106. 	[Title] Technical Diagnosis: Fallexperte-D of further knowledge sources (domain knowledge, common knowledge) is investigated in the [Abstract] Case based reasoning (CBR) uses the knowledge from former experiences ("known cases"). Since special knowledge of an expert is mainly subject to his experiences, the CBR techniques are a good base for the development of expert systems. We investigate the problem for technical diagnosis. Diagnosis is not considered as a classification task, but as a process to be guided by computer assisted experience. This corresponds to the flexible "case completion" approach. Flexibility is also needed for the expert view with predominant interest in the unexpected, unpredictible cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1121176. 	[Title] Utilizing Connectionist Learning Procedures in Symbolic Case Retrieval Nets [Abstract] This paper describes a method which, under certain circumstances, allows to automatically learn or adjust similarity measures. For this, ideas of connectionist learning procedures, in particular those related to Hebbian learning, are combined with a Case-Based Reasoning engine. 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 #1130653. 	[Title] Functional Programming by Analogy [Abstract] In this paper we describe how the principles of problem solving by analogy can be applied to the domain of functional program synthesis. For this reason, we treat programs as syntactical structures. We discuss two different methods to handle these structures: (a) a graph metric for determining the distance between two program schemes, and (b) the Structure Mapping Engine (an existing system to examine analogical processing). Furthermore we show experimental results and discuss them. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #566653. 	[Title] Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts? [Abstract] Judgments of similarity and soundness are important aspects of human analogical processing. This paper explores how these judgments can be modeled using SME, a simulation of Gentner's structure-mapping theory. We focus on structural evaluation, explicating several principles which psychologically plausible algorithms should follow. We introduce the Specificity Conjecture, which claims that naturalistic representations include a preponderance of appearance and low-order information. We demonstrate via computational experiments that this conjecture affects how structural evaluation should be performed, including the choice of normalization technique and how the systematicity preference is implemented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1130657. 	[Title] Learning from Examples: Reminding or Heuristic Switching? [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116839. 	[Title] Modeling Analogical Problem Solving in a Production System Architecture [Abstract] This research is supported by a National Science Foundation Fellowship awarded to Dario Salvucci and Office of Naval Research grant N00014-96-1-0491 awarded to John Anderson. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, the Office of Naval Research, or the United States government. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #385067. 	[Title] CBR for Document Retrieval: The FAllQ Project [Abstract] This paper reports about a project on document retrieval in an industrial setting. The objective is to provide a tool that helps finding documents related to a given query, such as answers in Frequently Asked Questions databases. A CBR approach has been used to develop a running prototypical system which is currently under practical evaluation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1138091. 	[Title] Use of Analogy in Automated Theorem Proving [Abstract] Technical Report ATP-90, Artificial Intelligence Laboratory, University of Texas at Austin. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #112813. 	[Title] REPRO: Supporting Flowsheet Design by Case-Base Retrieval [Abstract] Case-Based Reasoning (CBR) paradigm is very close to the designer behavior during the conceptual design, and seems to be a fruitable computer aided-design approach if a library of design cases is available. The goal of this paper is to presents the general framework of a case-based retrieval system: REPRO, that supports chemical process design. The crucial problems like the case representation and structural similarity measure are widely described. The presented experimental results and the expert evaluation shows usefulness of the described system in real world problems. The papers ends with discussion concerning research problems and future work. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10798. 	[Title] Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual [Abstract]  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 #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 #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 #5064. 	[Title] Case Retrieval Nets: Basic Ideas and Extensions [Abstract] An efficient retrieval of a relatively small number of relevant cases from a huge case base is a crucial subtask of Case-Based Reasoning. In this article, we present Case Retrieval Nets (CRNs), a memory model that has recently been developed for this task. The main idea is to apply a spreading activation process to a net-like case memory in order to retrieve cases being similar to a posed query case. We summarize the basic ideas of CRNs, suggest some useful extensions, and present some initial experimental results which suggest that CRNs can successfully handle case bases larger than considered usually in the CBR community. 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 #144330. 	[Title] Utilizing Prior Concepts for Learning [Abstract] The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe a transference bias and present M-FOCL, a Horn clause relational learning algorithm, that utilizes this bias to learn multiple concepts. We provide preliminary empirical evaluation to show the effects of biasing previous information on noise-free and noisy data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119180. 	[Title] Concept Sharing: A Means to Improve Multi-Concept Learning [Abstract] This paper describes several means for sharing between related concepts to improve learning in the same domain. The sharing comes in the form of substructures or possibly entire structures of previous concepts which may aid in learning other concepts. These substructures highlight useful information in the domain. Using two domains, we evaluate the effectiveness of concept sharing with respect to accuracy, concept size, search complexity, and noise resistance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5069. 	[Title] Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains [Abstract] This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #395725. 	[Title] Distributed Representations and Nested Compositional Structure [Abstract]  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 #5075. 	[Title] An Investigation of Marker-Passing Algorithms for Analogue Retrieval [Abstract] If analogy and case-based reasoning systems are to scale up to very large case bases, it is important to analyze the various methods used for retrieving analogues to identify the features of the problem for which they are appropriate. This paper reports on one such analysis, a comparison of retrieval by marker passing or spreading activation in a semantic network with Knowledge-Directed Spreading Activation, a method developed to be well-suited for retrieving semantically distant analogues from a large knowledge base. The analysis has two complementary components: (1) a theoretical model of the retrieval time based on a number of problem characteristics, and (2) experiments showing how the retrieval time of the approaches varies with the knowledge base size. These two components, taken together, suggest that KDSA is more likely than SA to be able to scale up to retrieval in large knowledge bases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153877. 	[Title] Adapting Abstract Knowledge [Abstract] For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the extent that its cases in memory can be efficiently adapted to fit a wide range of new situations. In this paper, we address the task of adapting abstract knowledge about planning to fit specific planning situations. First we show that adapting abstract cases requires reconciling incommensurate representations of planning situations. Next, we describe a representation system, a memory organization, and an adaptation process tailored to this requirement. Our approach is implemented in brainstormer, a planner that takes abstract advice. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153879. 	[Title] Role of Stories 1 [Abstract] PO Box 600 Wellington New Zealand Tel: +64 4 471 5328 Fax: +64 4 495 5232 Internet: Tech.Reports@comp.vuw.ac.nz Technical Report CS-TR-92/4 October 1992 Abstract People often give advice by telling stories. Stories both recommend a course of action and exemplify general conditions in which that recommendation is appropriate. A computational model of advice taking using stories must address two related problems: determining the story's recommendations and appropriateness conditions, and showing that these obtain in the new situation. In this paper, we present an efficient solution to the second problem based on caching the results of the first. Our proposal has been implemented in brainstormer, a planner that takes abstract advice. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #566488. 	[Title] REPRESENTING PHYSICAL AND DESIGN KNOWLEDGE IN INNOVATIVE DESIGN [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #272345. 	[Title] CBR on Semi-structured Documents: The ExperienceBook and the FAllQ Project [Abstract] In this article, we present a case-based approach on flexible query answering systems in two different application areas: The ExperienceBook supports technical diagnosis in the field of system administration. In the FAllQ project we use our CBR system for document retrieval in an industrial setting. The objective of these systems is to manage knowledge stored in less structured documents. The internal case memory is implemented as a Case Retrieval Net. This allows to handle large case bases with an efficient retrieval process. In order to provide multi user access we chose the client server model combined with a web interface. Please summarise all the given information.

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 #177115. 	[Title] Case Retrieval Nets: Foundations, Properties, [Abstract] Implementation, and Results 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.

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 #1153889. 	[Title] Is analogical problem solving always analogical? The case for imitation. Second draft Is analogical problem [Abstract] HCRL Technical Report 97 By Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1116397. 	[Title] Design, Analogy, and Creativity [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 #1108597. 	[Title] Analogical Problem Solving by Adaptation of Schemes [Abstract] We present a computational approach to the acquisition of problem schemes by learning by doing and to their application in analogical problem solving. Our work has its background in automatic program construction and relies on the concept of recursive program schemes. In contrast to the usual approach to cognitive modelling where computational models are designed to fit specific data we propose a framework to describe certain empirically established characteristics of human problem solving and learning in a uniform and formally sound way. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #221302. 	[Title] Task-Oriented Knowledge Acquisition and Reasoning for Design Support Systems [Abstract] We present a framework for task-driven knowledge acquisition in the development of design support systems. Different types of knowledge that enter the knowledge base of a design support system are defined and illustrated both from a formal and from a knowledge acquisition vantage point. Special emphasis is placed on the task-structure, which is used to guide both acquisition and application of knowledge. Starting with knowledge for planning steps in design and augmenting this with problem-solving knowledge that supports design, a formal integrated model of knowledge for design is constructed. Based on the notion of knowledge acquisition as an incremental process we give an account of possibilities for problem solving depending on the knowledge that is at the disposal of the system. Finally, we depict how different kinds of knowledge interact in a design support system. ? This research was supported by the German Ministry for Research and Technology (BMFT) within the joint project FABEL under contract no. 413-4001-01IW104. Project partners in FABEL are German National Research Center of Computer Science (GMD), Sankt Augustin, BSR Consulting GmbH, Munchen, Technical University of Dresden, HTWK Leipzig, University of Freiburg, and University of Karlsruhe. 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 #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 #28026. 	[Title] Case Retrieval Nets Applied to Large Case Bases [Abstract] This article presents some experimental results obtained from applying the Case Retrieval Net approach to large case bases. The obtained results suggest that CRNs can successfully handle case bases larger than considered in other reports. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30973. 	[Title] In  Estimating analogical similarity by dot-products of Holographic Reduced Representations. [Abstract] Models of analog retrieval require a computationally cheap method of estimating similarity between a probe and the candidates in a large pool of memory items. The vector dot-product operation would be ideal for this purpose if it were possible to encode complex structures as vector representations in such a way that the superficial similarity of vector representations reflected underlying structural similarity. This paper describes how such an encoding is provided by Holographic Reduced Representations (HRRs), which are a method for encoding nested relational structures as fixed-width distributed representations. The conditions under which structural similarity is reflected in the dot-product rankings of Please summarise all the given information.

Paper #5062 has the title `A Memory Model for Case Retrieval by Activation Passing  
` and the abstract `nan`.
Question: Which category should Paper #5062 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #5062 has the title `A Memory Model for Case Retrieval by Activation Passing  
` and the abstract `nan`.
Question: Which category should Paper #5062 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