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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A Robotic Scenario for Programmable Fixed-Weight Neural Networks Exhibiting Multiple Behaviors
verfasst von : Guglielmo Montone, Francesco Donnarumma, Roberto Prevete
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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Artificial neural network architectures are systems which usually exhibit a
unique/special
behavior on the basis of a fixed structure expressed in terms of parameters computed by a training phase. In contrast with this approach, we present a robotic scenario in which an artificial neural network architecture, the Multiple Behavior Network (MBN), is proposed as a robotic controller in a simulated environment. MBN is composed of two Continuous-Time Recurrent Neural Networks (CTRNNs), and is organized in a hierarchial way: Interpreter Module (
IM
) and Program Module (
PM
).
IM
is a fixed-weight CTRNN designed in such a way to behave as an
interpreter
of the signals coming from
PM
, thus being able to switch among different behaviors in response to the
PM
output
programs
. We suggest how such an MBN architecture can be incrementally trained in order to show and even acquire new behaviors by letting
PM
learn new programs, and without modifying
IM
structure.