NDLI: Intelligent module for planning/control of master-dependent systems

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Intelligent module for planning/control of master-dependent systems

Content Provider	ACM Digital Library
Author	Meystel, Alex
Abstract	An intelligent module is proposed in this paper which is capable of performing a joint recursive planning/control operation which propagates through the intelligent module at all planning/control levels simultaneously. Each of the actuators is equipped by an intelligent module, and all of these modules are working independently and concurrently. The model of the world is being constantly updated based upon vision and a multiplicity of other available sensors, and at various resolutions is submitted to each of the intelligent modules as applied to particular properties of the link being controlled by this module. Together these intelligent modules are working as a team of the actuators controllers, and all decisions are constantly negotiated among the members of the team. Each of the resolutional levels within the actuator intelligent modules is using the following set of planning/control tools: learning rule base, learning heuristic search, and situational decision generator which are first applied simultaneously, and then one of them takes over. A neural network is collecting information about the progress and the results of planning/control processes, and is modifying heuristics, as well as enriching the system of rules. The same neural network is used for supplying the provisional analytical model required for the lowest levels of execution control. Provisional analytical models are applied in a simple form which allows for simple real-time controller operation, and the parameters of this provisional model are constantly being updated by the neural network.
Starting Page	620
Ending Page	628
Page Count	9
File Format	PDF
ISBN	0897912713
DOI	10.1145/51909.51980
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1988-06-01
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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