Toward a Neural-Symbolic Sigma: Introducing Neural Network Learning (bibtex)
by Paul S. Rosenbloom, Abram Demski, Volkan Ustun
Abstract:
Building on earlier work extending Sigma’s mixed (symbols + probabilities) graphical band to inference in feedforward neural networks, two forms of neural network learning – target propagation and backpropagation – are introduced, bringing Sigma closer to a full neural-symbolic architecture. Adapting Sigma’s reinforcement learning (RL) capability to use neural networks in policy learning then yields a hybrid form of neural RL with probabilistic action modeling.
Reference:
Toward a Neural-Symbolic Sigma: Introducing Neural Network Learning (Paul S. Rosenbloom, Abram Demski, Volkan Ustun), In Proceedings of the 15th Annual Meeting of the International Conference on Cognitive Modelling, 2002–2017 EasyChair, 2017.
Bibtex Entry:
@inproceedings{rosenbloom_toward_2017,
	address = {Coventry, United Kingdom},
	title = {Toward a {Neural}-{Symbolic} {Sigma}: {Introducing} {Neural} {Network} {Learning}},
	url = {http://cs.usc.edu/~rosenblo/Pubs/ESNNL%20D.pdf},
	abstract = {Building on earlier work extending Sigma’s mixed (symbols + probabilities) graphical band to inference in feedforward neural networks, two forms of neural network learning – target propagation and backpropagation – are introduced, bringing Sigma closer to a full neural-symbolic architecture. Adapting Sigma’s reinforcement learning (RL) capability to use neural networks in policy learning then yields a hybrid form of neural RL with probabilistic action modeling.},
	booktitle = {Proceedings of the 15th {Annual} {Meeting} of the {International} {Conference} on {Cognitive} {Modelling}},
	publisher = {2002–2017 EasyChair},
	author = {Rosenbloom, Paul S. and Demski, Abram and Ustun, Volkan},
	month = jul,
	year = {2017},
	keywords = {UARC, Virtual Humans}
}
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