Planning with a functional neural network architecture

Introduces the concept of planning in an interactive environment between two systems: the challenger and the responder. The responder's task is to produce behavior that relates to the challenger's behavior through some response function. In this setup, we concentrate planning on the respon...

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Bibliographic Details
Published in:IEEE transactions on neural networks 1999-01, Vol.10 (1), p.115-127
Main Authors: Panagiotopoulos, D.A., Newcomb, R.W., Singh, S.K.
Format: Article
Language:English
Subjects:
Applied sciences
Architecture
Artificial neural networks
Control systems
Dynamical systems
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Interactive
Navigation
Neural networks
Neurofeedback
Nonlinear systems
Output feedback
Path planning
Process planning
Response functions
Robotics and automation
Tasks
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Summary:Introduces the concept of planning in an interactive environment between two systems: the challenger and the responder. The responder's task is to produce behavior that relates to the challenger's behavior through some response function. In this setup, we concentrate planning on the responder's actions and use the produced plan in order to control the responder. In general, the responder is assumed to be a nonlinear system whose input-output (I/O) map may be expressed by a Volterra series. The planner uses an estimate of the challenger's future output sequence, the response function, and a model of the responder's I/O relation implemented through a functional artificial neural network (FANN) architecture, in order to produce the input sequence that will be applied to the responder in the future, in parallel-time with the challenger's corresponding output sequence. The responder accepts input from the planner, which may be combined with feedback information, in order to produce an output sequence that relates to the challenger's output sequence according to the response function. The importance of planning for the generation of smooth behavior is discussed, and the effectiveness of the planner's implementation using neural network technology is demonstrated with an example.
ISSN:1045-9227
1941-0093
DOI:10.1109/72.737498