KNACK—Report-driven knowledge acquisition

Klinker, Georg; Bentolila, Joel; Genetet, Serge; Grimes, Michael; McDermott, John

doi:10.1016/s0020-7373(87)80036-6

Cited by 55 publications

(18 citation statements)

References 2 publications

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“…Most KA tools require expert users to supply the description of a case or knowledge, outlining either completely or partially the attributes or concepts involved in the knowledge tasks or applications-for example, MORE (Kahn et al 1985a;1985b), AQUINAS (Boose et al 1995, Boose andBradshaw 1987), MIKE (Eisenstadt and Brayshaw 1990), SALT (Marcus 1987), KITTEN , KNACK (Klinker et al 1987), KSS0 , ALTO (Major and Reichgelt 1990), CMBKATs (Charlet et al 1992), KATEMES (Dieng et al 1992), CERISE (Vicat et al 1993), KADS (Schreiber et al 1993;van Heijst et al 1997), and many others . However, none of these KA tools has dealt with image-based knowledge ontology and devised a tool that facilitates either the acquisition or the refinement of the knowledge.…”

Section: Related Workmentioning

confidence: 99%

ARKTOS: a knowledge engineering software tool for images

Soh¹,

Tsatsoulis²

2002

International Journal of Human-Computer Studies

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-The goal of our ARKTOS project is to build an intelligent knowledge-based system to classify satellite sea ice images. It involves acquiring knowledge from sea ice experts, quantifying such knowledge as computational entities, and ultimately building an intelligent classifier. In this paper we describe a two-stage knowledge engineering approach that facilitates explicit knowledge transfer, converting implicit visual cues and cognition of the experts to explicit attributes and rules implemented by the engineers. First, there is a prototyping stage that involves interviewing sea ice experts, transcribing the sessions, identifying descriptors and rules, designing and implementing the knowledge, and delivering the prototype. The objective of this stage is to obtain a modestly accurate classification system quickly. Second, there is a refinement stage that involves evaluating the prototype, refining the knowledge base, modifying the design, and re-evaluating the improved system. Since the refinement is evaluation-driven, the experts and the engineers are motivated explicitly to improve the knowledge base and are able to communicate with each other using a common, consistent platform. Moreover, since the classification result is immediately available, both sides are able to efficiently assess the correctness of the system. To facilitate the knowledge engineering of the second stage, we have designed and built three Java-based graphical user interfaces: arktosGUI, arktosViewer, and arktosEditor. arktosGUI concentrates on feature-based refinement of specific attributes and rules. arktosViewer deals with regional evaluation. arktosEditor has a rule indexing and search mechanism and knowledge base editing capabilites. INTRODUCTIONThe goal of our ARKTOS (Advanced Reasoning using Knowledge for Typing Of Sea ice) project is to perform automated, intelligent satellite sea ice image classification. Our approach is to acquire the classification knowledge from sea ice geophysicists and photo-interpreters and implement the knowledge in a rule-based system that also utilizes image processing methodologies.To facilitate a knowledge transfer from the sea ice experts to the knowledge and software engineers, we employ a two-stage knowledge engineering approach that includes rapid prototyping and evaluation-driven refinement. The rapid prototyping allows the experts and the engineers to design and implement a modestly accurate classification system. Given the functional system, the experts are then able to evaluate its classification results. To facilitate the evaluation process, we create Java-based software tools such that implicit visual cues and cognition of the experts can be explicitly expressed and fine-tuned in attributes and rules. The prototyping stage involves

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Section: Related Workmentioning

confidence: 99%

ARKTOS: a knowledge engineering software tool for images

Soh¹,

Tsatsoulis²

2002

International Journal of Human-Computer Studies

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“…It performed dynamic analysis on test cases to help: 1) discover missing knowledge, 2) guide in the revision of support values, and 3) further disambiguate the network. After initial knowledge acquisition, KNACK (Klinker et al, 1987) would search the knowledge base for gaps or conflicts and prompt the developer for corrections.…”

Section: Knowledge Acquisition/refinement Toolsmentioning

confidence: 99%

Guidelines for the verification and validation of expert system software and conventional software: Survey and documentation of expert system verification and validation methodologies. Volume 3

Groundwater¹,

Miller²,

Mirsky³

1995

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“…Scientists at Boeing have participated in the development, application, and evaluation of several automated knowledge acquisition tools, including ETS (Expertise Transfer System; Boose, 1985Boose, , 1986, Aquinas (Boose & Bradshaw, 1987;, SALT (Marcus, 1987;Marcus, 1988), and KNACK (Kitto, 1988;Klinker, Boyd, Genetet, & McDermott, 1988).…”

Section: Centermentioning

confidence: 99%

Knowledge acquisition techniques for decision analysis using AXOTL and AQUINAS

Bradshaw¹,

Convington²,

Russo³

et al. 1991

Knowledge Acquisition

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The effective application of current decision tree and influence diagram software requires a relatively high level of sophistication in the theory and practice of decision analysis.Research on intelligent decision systems aims to lower the cost and amount of training required to use these methods through the use of knowledge-based systems; however, application prototypes implemented to date have required time-consuming and tedious handcrafting of knowledge bases. This paper describes the development of DDUCKS, an "open architecture" problem-modeling environment that integrates components from Axotl, a knowledge-based decision analysis workbench, with those of Aquinas, a knowledge acquisition workbench based on personal construct theory. The knowledge base tools in Axotl can be configured with knowledge to provide guidance and help in formulating, evaluating, and refining decision models represented in influence diagrams. Knowledge acquisition tools in DDUCKS will allow the knowledge to be efficiently modeled, more easily maintained, and thoroughly tested. INTRODUCTION Progress in Automated Decision AnalysisThe Boeing Company has an urgent need for advanced automated decision support applications. Rapid growth in the complexity of strategic and tactical decisions has outstripped the capacity of conventional decision aids. (Langlotz, Shortliffe, and Fagan, 1986). Finally, knowledge-based system development environments do not generally provide facilities for integrating historical data with expert judgment (Spiegelhalter, Franklin, and Bull, 1990).One of the most promising approaches to dealing with decision complexity in a consistent, general-purpose manner is decision analysis (Howard, 1966;Keeney & Raiffa, 1976;Raiffa, 1968). In the past few years, researchers and developers have made important theoretical advances and have implemented several successful systems for automated support of the decision analysis process (reviews by Horvitz, Breese, & Henrion, 1988;Pearl, 1988;Neapolitan, 1990). Although a thorough discussion is beyond the scope of this paper, we wish to review three of the important developments that have led to the current state of the art. Following this, we will explain why we think that the development of automated knowledge acquisition tools is crucial to the future of efforts to deliver decision analysis methodology to a wider spectrum of decision makers and domains.Decision tree software. The development of decision tree software ( Figure 1) represented an important milestone in automated decision analysis support (Olmsted, 1982;McNamee & Celona, 1987). Through the use of general-purpose commercial tools, decision analysis techniques have become more widely known and used than ever before. At the same time,there are several drawbacks to the use of decision trees as a representation device. For one thing, they grow exponentially with problem size, making them impractical for problems of significant size. Additionally, the tree metaphor for decision problems often leads participants t...

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KNACK—Report-driven knowledge acquisition

Cited by 55 publications

References 2 publications

ARKTOS: a knowledge engineering software tool for images

ARKTOS: a knowledge engineering software tool for images

Guidelines for the verification and validation of expert system software and conventional software: Survey and documentation of expert system verification and validation methodologies. Volume 3

Knowledge acquisition techniques for decision analysis using AXOTL and AQUINAS

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