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Knowledge-based load leveling and task allocation in human-machine systemsConventional human-machine systems use task allocation policies which are based on the premise of a flexible human operator. This individual is most often required to compensate for and augment the capabilities of the machine. The development of artificial intelligence and improved technologies have allowed for a wider range of task allocation strategies. In response to these issues a Knowledge Based Adaptive Mechanism (KBAM) is proposed for assigning tasks to human and machine in real time, using a load leveling policy. This mechanism employs an online workload assessment and compensation system which is responsive to variations in load through an intelligent interface. This interface consists of a loading strategy reasoner which has access to information about the current status of the human-machine system as well as a database of admissible human/machine loading strategies. Difficulties standing in the way of successful implementation of the load leveling strategy are examined.
Document ID
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Chignell, M. H.
(California Univ. Los Angeles, CA, United States)
Hancock, P. A.
(California Univ. Los Angeles, CA, United States)
Date Acquired
August 12, 2013
Publication Date
May 1, 1986
Publication Information
Publication: NASA. Ames Research Center, 21st Annual Conference on Manual Control
Subject Category
Man/System Technology And Life Support
Accession Number
Distribution Limits
Work of the US Gov. Public Use Permitted.
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