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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/656

Title: An Intelligent Man-Machine Dialogue System Based on AI
Authors: KUIJPERS, Bart
Dockx, Kris
Issue Date: 1998
Publisher: Springer-Verlag
Citation: Applied Intelligence, 8(3). p. 235-245
Abstract: Abstract We describe the modular architecture of a generic dialogue system that assists a user/operator in performing a task with a tool. This coaching system is named CALLIOPE after the Greek goddess of eloquence. It aims at being an active partner in an intelligent man-machine dialogue. The intelligent dimension of the coaching system is reflected by its ability to adapt to the user and the situation at hand. The CALLIOPE system contains an explicit user model and world model to situate its dialogue actions. A plan library allows it to follow loosely predetermined dialogue scenarios. The heart of the coaching system is an AI planning module, which plans a series of dialogue actions. We present a coherent set of three dialogue or speech actions that will make up the physical form of the man-machine communication.The use of the AI planning paradigm as a basis for man-machine interaction is motivated by research in various disciplines, as e.g., AI, Cognitive Science and Social Sciences. Starting from the man-man communication metaphor, we can view the thinking before speaking of a human communication partner as constructing an underlying plan which is responsible for the purposiveness, the organisation and the relevance of the communication. CALLIOPE has been fully implemented and tested on theoretical examples. At present, also three tailored versions of CALLIOPE are in operational use in different industrial application domains: operator support for remedying tasks in chemical process industry, operator support for a combined task of planning, plan execution and process control in the area of chemical process development, and thirdly decision support in production scheduling
Notes: issn:1573-7497 (Online)
URI: http://hdl.handle.net/1942/656
DOI: 10.1023/A:1008268417191
ISSN: 0924-669X
Type: Journal Contribution
Appears in Collections: Research publications

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