Abstract
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.
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Montone, G., Donnarumma, F., Prevete, R. (2011). A Robotic Scenario for Programmable Fixed-Weight Neural Networks Exhibiting Multiple Behaviors. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_26
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DOI: https://doi.org/10.1007/978-3-642-20282-7_26
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