TY - GEN
T1 - A new approach for supervised learning based influence value reinforcement learning
AU - Valdivia, André
AU - Quispe, Jose Herrera
AU - Barrios-Aranibar, Dennis
N1 - Publisher Copyright:
© Association for Computing Machinery. All rights reserved.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - The neural self-organization, is an innate feature of the mammal's brains, and is necessary for its operation. The most known neuronal models that use this characteristic are the self-organized maps (SOM) and the adaptive resonance theory (ART), but those models, did not take the neuron as a processing unit, as the biological counterpart. On the other hand, the influence value learning paradigm [1], used in multi-agent environments, proof that agents can communicate with each other [2]; and they can self-organize to assign tasks; without any interference. Motivated by this missing feature in artificial networks, and with the influence value reinforcement learning algorithm; a new approach to supervised learning was modeled using the neuron as an agent learning by reinforcement.
AB - The neural self-organization, is an innate feature of the mammal's brains, and is necessary for its operation. The most known neuronal models that use this characteristic are the self-organized maps (SOM) and the adaptive resonance theory (ART), but those models, did not take the neuron as a processing unit, as the biological counterpart. On the other hand, the influence value learning paradigm [1], used in multi-agent environments, proof that agents can communicate with each other [2]; and they can self-organize to assign tasks; without any interference. Motivated by this missing feature in artificial networks, and with the influence value reinforcement learning algorithm; a new approach to supervised learning was modeled using the neuron as an agent learning by reinforcement.
KW - Multi-agent
KW - Neural networks
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85060432650&partnerID=8YFLogxK
U2 - 10.1145/3184066.3184094
DO - 10.1145/3184066.3184094
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85060432650
T3 - ACM International Conference Proceeding Series
SP - 24
EP - 28
BT - 2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018
PB - Association for Computing Machinery
T2 - 2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018
Y2 - 2 February 2018 through 4 February 2018
ER -