Search (12 results, page 1 of 1)

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Abstract

Proposes a new architecture combining rule-based reasoning (RBR), case based reasoning (CBR) and knowledge acquisition technology in a system which solves pattern search problems. Details the pattern search problem, the system architecture and functions, error repair method via case-based reasoning, the knowledge acquisition method, system evaluation, and discusses related work

Date

6. 3.1997 16:22:15

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Abstract

Presents an architeture for combining rule-based and case-based reasoning. It is applied to the problem of name pronunciation. Presents the system independent of the domain of name pronunciation. Describes the Anapron system, which instantiates the architecture for name pronunciation. Presents a set of experiments on Anapron, the key result being an empirical demonstration of the improvement obtained by combining rules and cases. Discusses related work

Date

6. 3.1997 16:22:15

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Abstract

Introduces a new method for continuous case-based reasoning, and discusses its applications to the dynamic selection, modification and acquisition of robot bahaviours in an autonomous navigation system, SINS (self-improving navigation system): The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. Discusses case-based reasoning issues addressed by this research

Date

6. 3.1997 16:22:15

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Abstract

We present 2 techniques for reasoning from cases to solve classification tasks: induction and case-based reasoning. We contrast the 2 technologies (that are often confused) and show how they complement each other. Based on this, we describe how they are integrated in one single platform for reasoning from cases: the INRECA system

Source

Information systems and data analysis: prospects - foundations - applications. Proc. of the 17th Annual Conference of the Gesellschaft für Klassifikation, Kaiserslautern, March 3-5, 1993. Ed.: H.-H. Bock et al

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Abstract

Discusses a hybrid approach combining case-based reasoning and information retrieval for the retrieval of full text documents. It takes as input a standard symbolic representation of a problem case and retrieves text of relevant cases from a document collection dramatically larger than the case base available to the CBR system. It works by performing a standard HYPO style analysis and uses the texts associated with important classes of cases found in this analysis to seed a modified version of INQUERY's relevance feedback mechanism in order to generate a query composed of individual terms or pairs of terms. It exteds the reach of CBR to much larger corpora, and it anbales the injection of knowledge based techniques into traditional IR. Describes the CBR-IR approach and reports on-going experiments

Footnote

Contribution to a special issue on the application of artificial intelligence to information retrieval

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Abstract

Presents a fuzzy constraint satisfaction system which can be used in a distributive environment where large problems can be broken down into smaller constraint networks for easier processing. Identifies contexts which exist within the constraint satisfaction system. Context based reasoning is identified both within and among constraint networks. Outlines the motviation behind the research and describes the fuzzy constraint satisfaction system FuzCon. Points out 3 mappings of the context-based reasoning 'ist' operator to fuzzy constraints and presents an example of designing a printed wiring board

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Abstract

Case based reasoning offers a new approach for developing knowledge based systems. Most systems are currently prototypes. A number of research issues need to be resolved to facilitate the transition of these prototypes to large application systems, the primary issue being to develop data management support for these prototypes. Analyzes this data management support and proposes a new concept called a casease management system to perfom data management functions for case based systems

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Abstract

Reports on research which studied how the incorporation of case-specific, episodic, knowledge enables decision-support systems to become more robust and to adapt to a changing environment by continuously retaining new problem-solving cases as they occur during normal system operation

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Abstract

Case Based Reasoning (CBR) is discussed in connection with a wide variety of knowledge aspects. Obviously knowledge acquisition under conditions of rapid and increasingly disruptive change will necessarily rely on the proper use of case experience. The following remarks emerged from practice oriented teaching/learning in the domians of social sciences, practical philosophy, didactics and epistemology. The main interest will be directed to methodical concepts of knowledge transfer, in particular to didactics and learning within the teaching/learning system

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Abstract

Investigates the use of formal mathematical models in the design of interactive systems and argues for the development of generic models that describe the behaviour of a class of interactive systems. It is possible to construct a generic model of a class of interactive systems at an intermediate level of abstraction. Such a model would offer wider reusability than detailed specifications of a single system, but greater expressiveness and support for software development than fully generate abstract models. Reviews a number of existing models in the literature and presents a generic model of interactive case memories, a class of systems used in case-based reasoning

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Abstract

Mangelndes methodisches Bewußtsein in der Künstlichen Intelligenz hat zur Folge, daß Methoden, die auf einer einzigen zunächst vielversprechenden Idee beruhen, zugunsten anderer fallengelassen werden, sowie sich ernste Schwierigkeiten ergeben. Doch für Methoden gilt nicht das Alles-oder-Nichts-Prinzip; Ziel muß es daher sein, verschiedene sich hinsichtlich ihrer Anwendbarkeit einander ergänzende Methoden aufeinander abzustimmen und zu einem arbeitsfähigen System zu vereinen; das trifft insbesondere auf das bereits vom Ansatz her auf Methodenvielfalt angelegte fallbezogene Schlußfolgern zu. Die von den Kognitionswissenschaften gebotenen Voraussetzungen sind jedoch für solch ein Vorhaben nicht günstig. Es herrscht ein Theorien- und Modellwirrwar, das zu einem Wirrwar von Begriffen, Sichtweisen und Verfahren geführt hat. Die vorliegende Arbeit skizziert einen Ausweg aus dieser unbefriedigenden Situation. Sie orientiert sich an den Fragen 'welche Aufgaben sollen mit fallbezogenem Schlußfolgern gelöst werden?', 'durch welche Merkmale ist dieser Ansatz charakterisiert?' und beschäftigt sich abschließend mit den sich aus den Antworten ergebenden Folgerungen

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Source

Information processing and management. 29(1993) no.2, S.209-214