Journal ArticleDOI
Generalized case-based reasoning system for portfolio management
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TLDR
A portfolio management support system based upon the proposedGCBRS architecture is presented to demonstrate the feasibility of using GCBRS for developing a decision support system in a knowledge-poor and experience-poor domain.Abstract:
A case-based reasoning system (CBRS) is appropriate for an experince-rich domain, while a rule-based system performs reasonably well in a knowledge-rich application environment. Performance of a CBRS suffers when past experience is not readily available. A generalized case-based reasoning system (GCBRS) is proposed to remedy this weakness by incorporating domain theories represented as generalization rules. With these rules, previous experience (stored as cases) can be generalized so that the possibility of solving a new case is higher than it would be when case-based reasoning is used alone. The architecture and the inference mechanism of a GCBRS are discussed in this article. A portfolio management support system based upon the proposed GCBRS architecture is presented to demonstrate the feasibility of using GCBRS for developing a decision support system in a knowledge-poor and experience-poor domain. This article concludes with a discussion of future research.read more
Citations
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Journal ArticleDOI
Case-based reasoning supported by genetic algorithms for corporate bond rating
Kyung Shik Shin,Ingoo Han +1 more
TL;DR: A hybrid approach using genetic algorithms (GAs) to case-based retrieval process in an attempt to increase the overall classification accuracy and a machine learning approach using GAs to find an optimal or near optimal weight vector for the attributes of cases in case indexing and retrieving.
Journal ArticleDOI
A case-based approach using inductive indexing for corporate bond rating
Kyung-shik Shin,Ingoo Han +1 more
TL;DR: This paper investigates the effectiveness of inductive learning approach to case indexing process for business classification tasks and suggests this approach as a unifying framework to combine general domain knowledge and case-specific knowledge.
Journal ArticleDOI
Fuzzy indexing and retrieval in case-based systems
Bingchiang Jeng,Ting-Peng Liang +1 more
TL;DR: In this article, a new approach that integrates fuzzy set concepts into the case indexing and retrieval process is presented, which allows numerical features to be converted into fuzzy terms to simplify the matching process.
Journal ArticleDOI
Distributed intelligent executive information systems
Robert T. Chi,Efraim Turban +1 more
TL;DR: This framework illustrates how multiple resources can be combined for information processing in an EIS environment and allows multiple agents to work collaboratively to help complex information processing.
Journal ArticleDOI
Knowledge engineering for an intelligent case-based system for help desk operations
TL;DR: The objective of this project is to develop an automated case-based help desk system to support both call center personnel and customers, and to contribute to shortening the response time on incoming calls and reduce training time for new employees.
References
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Book ChapterDOI
A framework for representing knowledge
TL;DR: The enormous problem of the volume of background common sense knowledge required to understand even very simple natural language texts is discussed and it is suggested that networks of frames are a reasonable approach to represent such knowledge.
A framework for representing knowledge
TL;DR: The authors describes frame systems as a formalism for representing knowledge and then concentrates on the issue of what the content of knowledge should be in specific domains, arguing that vision should be viewed symbolically with an emphasis on forming expectations and then using details to fill in slots in those expectations.
Book
Principles of Artificial Intelligence
TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Book
Inside Case-Based Reasoning
TL;DR: CBR tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case, and is similar to the rule-induction algorithms of machine learning.
Book
Prolog Programming for Artificial Intelligence
TL;DR: The new edition of Prolog Guide to AI programming has been fully revised and extended to provide an even greater range of applications, enhancing its value as a stand-alone guide to Prolog, artificial intelligence, or AI programming.