ComputingComputer ScienceDecision Support SystemsFault-tolerant SystemsKnowledge RepresentationExpert SystemsFault DetectionFault DiagnosisFault ManagementFault ModelingRule-based RepresentationAutomated Fault ManagementCase-based Decision SupportCase-based Knowledge RepresentationCase-based Problem SolvingCase-based ReasoningCase-based Reasoning SupportHeterogeneous Networks
Most fault resolution systems for communications networks represent fault resolution expertise using a rule-based reasoning (RBR) paradigm. Although the RBR paradigm is appropriate for problem-solving tasks that are confined and well-understood, its limitations are: an inability to learn from experience; an inability to deal with novel problems; and the difficulty of updating the systems to keep up with rapidly changing domains such as expanding heterogeneous networks. The author describes a case-based reasoning (CBR) application that is less constrained by these limitations. The approach is to enhance a standard fault management system with a CBR component. CBR techniques are reviewed for retrieving, adapting, and embedding knowledge in a case library, and CRITTER, a CBR trouble ticketing system for managing and resolving network faults, is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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An introduction to case-based reasoning
Janet L. Kolodner · Artificial Intelligence Review · 1992 · 1K citations
CHEF: a model of case-based planning
Kristian J. Hammond · 1986 · 348 citations
Case-based reasoning: a research paradigm
Stephen Slade · AI Magazine · 1991 · 299 citations
A Logical Approach to Reasoning by Analogy
Todd Davies, Stuart Russell · SSRN Electronic Journal · 1987 · 104 citations · Full text