Towards knowledge acquisition by experts

Puppe, Frank; Gappa, Ute

doi:10.1007/bfb0025008

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Using the example, a better choice because it is esult, this means there must ng the specific IP address to edge has been identified as ert systems. Knowledge knowledge engineers, who g knowledge from experts edge engineers could be [42] explain that direct approach in terms of cost. hemselves to formalize their into an expert system.…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

Design considerations for a case-based reasoning engine for scenario-based cyber incident notification

Woskov

Grimaila

Mills

et al. 2011

2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)

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Virtually all modern organizations have embedded information systems into their core business processes as a means to increase operational efficiency, improve decision making quality, and minimize costs. Unfortunately, this dependence can place an organization's mission at risk if the confidentiality, integrity, or availability of a critical information resource has been lost or degraded. Within the military, this type of incident could ultimately result in serious consequences including physical destruction and loss of life. To reduce the likelihood of this outcome, personnel must be informed about cyber incidents, and their potential consequences, in a timely and relevant manner so that appropriate contingency actions can be taken. In this paper, we identify criteria for improving the relevance of incident notification, propose the use of case-based reasoning (CBR) for contingency decision support, and identify key design considerations for implementing a CBR system used to deliver relevant notification following a cyber incident.

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Section: Knowledge Acquisitionmentioning

confidence: 99%

Design considerations for a case-based reasoning engine for scenario-based cyber incident notification

Woskov

Grimaila

Mills

et al. 2011

2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS)

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show abstract

“…The knowledge acquisition tools can be generated from a declarative description of the knowledge representation and the views acquired in the knowledge editors. The library of CRLM currently contain mechanisms for heuristic ([PuG92], [GPS93]) case-based [PuG91] and set-covering classification [Pup93], methods for assignment (propose-andexchange [PoP92]) and for simple configuration (propose-and-revise [PFL+94]). In MIKE, reuse is enabled by the very-high level language KARL which makes it possible to quickly write powerful (though inefficient) specifications of problemsolving methods and domain knowledge.…”

Section: Conclusion: Contradiction or Complement?mentioning

confidence: 99%

A comparison of two approaches to model-based knowledge acquisition

Fensel¹,

Poeck

1994

Lecture Notes in Computer Science

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Abstract. This paper discusses and compares two different approaches to model-based knowledge acquisition. That is, we regard the Model-based and Incremental Knowledge Engineering (MIKE) approach and the Configurable Role-limiting Method approach (CRLM). MIKE is based on the distinction of different phases in the software development process. It uses the formal and operational knowledge specification language KARL allowing a precise and unique description of a model of expertise which is the outcome of the analysis phase. CRLM is based on the rolelimiting method approach. Role limiting shells are implementations of strong problem-solving methods and substantially simplify knowledge acquisition through guidance by predefined models of problem-solving and by sophisticated graphical user interfaces. The main disadvantages, namely inflexibility and brittleness, are to some degree overcome by the CRLM where the shell's problem-solving methods are split into smaller parts, which can then be reconfigured allowing the integration of new methods or other method combinations. Although these two approaches are often discussed as contradictory, we, however, experienced that both approaches complete each other very well. As an outcome of our comparison, we outline topics of future research for both approaches.

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Puppe¹

1993

Systematic Introduction to Expert Systems

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Towards knowledge acquisition by experts

Cited by 6 publications

References 10 publications

Design considerations for a case-based reasoning engine for scenario-based cyber incident notification

Design considerations for a case-based reasoning engine for scenario-based cyber incident notification

A comparison of two approaches to model-based knowledge acquisition

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