The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive preprocessing to bridge the representation ga...
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...
We present a model of creating a hierarchical set of rules that encode generalizations and exceptions derived from induction learning. The rules use the input features directly an...
Abstract. Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic appro...
Neural-symbolic integration concerns the integration of symbolic and connectionist systems. Distributed knowledge representation is traditionally seen under a purely symbolic pers...