Uncertainty in Artificial Intelligence
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Decision Methods for Adaptive Task-Sharing in Associate Systems
Thomas Paterson, Michael Fehling
Abstract:
This paper describes some results of research on associate systems: knowledge-based systems that flexibly and adaptively support their human users in carrying out complex, time-dependent problem-solving tasks under uncertainty. Based on principles derived from decision theory and decision analysis, a problem-solving approach is presented which can overcome many of the limitations of traditional expert-systems. This approach implements an explicit model of the human user's problem-solving capabilities as an integral element in the overall problem solving architecture. This integrated model, represented as an influence diagram, is the basis for achieving adaptive task sharing behavior between the associate system and the human user. This associate system model has been applied toward ongoing research on a Mars Rover Manager's Associate (MRMA). MRMA's role would be to manage a small fleet of robotic rovers on the Martian surface. The paper describes results for a specific scenario where MRMA examines the benefits and costs of consulting human experts on Earth to assist a Mars rover with a complex resource management decision.
Keywords:
Pages: 238-243
PS Link:
PDF Link: /papers/92/p238-paterson.pdf
BibTex:
@INPROCEEDINGS{Paterson92,
AUTHOR = "Thomas Paterson and Michael Fehling",
TITLE = "Decision Methods for Adaptive Task-Sharing in Associate Systems",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "238--243"
}


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