Causal model-based knowledge acquisition tools: Discussion of experiments

Charlet, Jean; Krivine, Jean-Paul; Reynaud, Chantal

doi:10.1007/3-540-55546-3_48

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Στο στάδιο της Ανάλυσης Γνώσης αναλύεται η αποσπασματική γνώση, που συγκεντρώθηκε στη διαδικασία εκμαίευσης [219]. Η ανάλυση βασίζεται στη μελέτη των λέξεων και των εννοιών που αυτές αντιπροσωπεύουν.…”

Section: μεθοδολογίες εκμαίευσης γνώσηςunclassified

Σύστημα Υποστήριξης Λήψεως Απόφασης Για Τη Διαχείριση Δασικών Οικοσυστημάτων Βασισμένο Στα Γεωγραφικά Πληροφοριακά Συστήματα Και Στα Συστήματα Γνώσης

Kaloudis¹,

Καλούδης²

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Section: μεθοδολογίες εκμαίευσης γνώσηςunclassified

Σύστημα Υποστήριξης Λήψεως Απόφασης Για Τη Διαχείριση Δασικών Οικοσυστημάτων Βασισμένο Στα Γεωγραφικά Πληροφοριακά Συστήματα Και Στα Συστήματα Γνώσης

Kaloudis¹,

Καλούδης²

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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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ACTE: a Causal Model-Based Knowledge Acquisition Tool

Charlet

1993

Second Generation Expert Systems

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Causal model-based knowledge acquisition tools: Discussion of experiments

Cited by 3 publications

References 19 publications

Σύστημα Υποστήριξης Λήψεως Απόφασης Για Τη Διαχείριση Δασικών Οικοσυστημάτων Βασισμένο Στα Γεωγραφικά Πληροφοριακά Συστήματα Και Στα Συστήματα Γνώσης

Σύστημα Υποστήριξης Λήψεως Απόφασης Για Τη Διαχείριση Δασικών Οικοσυστημάτων Βασισμένο Στα Γεωγραφικά Πληροφοριακά Συστήματα Και Στα Συστήματα Γνώσης

ARKTOS: a knowledge engineering software tool for images

ACTE: a Causal Model-Based Knowledge Acquisition Tool

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