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Investigation of automated task learning, decomposition and schedulingThe details and results of research conducted in the application of neural networks to task planning and decomposition are presented. Task planning and decomposition are operations that humans perform in a reasonably efficient manner. Without the use of good heuristics and usually much human interaction, automatic planners and decomposers generally do not perform well due to the intractable nature of the problems under consideration. The human-like performance of neural networks has shown promise for generating acceptable solutions to intractable problems such as planning and decomposition. This was the primary reasoning behind attempting the study. The basis for the work is the use of state machines to model tasks. State machine models provide a useful means for examining the structure of tasks since many formal techniques have been developed for their analysis and synthesis. It is the approach to integrate the strong algebraic foundations of state machines with the heretofore trial-and-error approach to neural network synthesis.
Document ID
19900017172
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Livingston, David L. (Old Dominion Univ. Norfolk, VA, United States)
Serpen, Gursel (Old Dominion Univ. Norfolk, VA, United States)
Masti, Chandrashekar L. (Old Dominion Univ. Norfolk, VA, United States)
Date Acquired
September 6, 2013
Publication Date
July 1, 1990
Subject Category
Behavioral Sciences
Report/Patent Number
NAS 1.26:186791
NASA-CR-186791
Report Number: NAS 1.26:186791
Report Number: NASA-CR-186791
Accession Number
90N26488
Funding Number(s)
CONTRACT_GRANT: NAG1-962
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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