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A constructivist approach for a self-adaptive decision-making system: application to road traffic control

Maxime Guériau 1, 2 Frédéric Armetta 1 Salima Hassas 1 Romain Billot 3, 4 Nour-Eddin El Faouzi 2
1 SMA - Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : The relevance of decision making in autonomous systems is intrinsically related to the system capacity to discriminate its perception-action states. This is particularly challenging in unknown and changing complex environments, where providing a complete a priori representation to the system is not possible. To illustrate the problem, let us consider a decentralized control of road traffic, where a control device of the distributed infrastructure locally controls traffic, by learning to construct a precise representation (perception-action states) of the traffic state. In this context, it is challenging to define from prior knowledge a relevant representation of the traffic state that enables an efficient recommendation-based control. Without considering a prior domain-knowledge representation, we propose an approach able to combine a set of existing traditional unsupervised learning methods that collaborate as a population of agents in order to build an efficient representation. Our approach follows a constructivist learning perspective, where each agent produces a possible discretization of the raw sensed data. Thanks to a multi-agent reinforcement learning process, the population is able to collectively build a representation that combines the good capacities of the individual ones.
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Contributor : Maxime Guériau <>
Submitted on : Monday, September 26, 2016 - 2:36:22 PM
Last modification on : Wednesday, August 5, 2020 - 3:42:57 AM



Maxime Guériau, Frédéric Armetta, Salima Hassas, Romain Billot, Nour-Eddin El Faouzi. A constructivist approach for a self-adaptive decision-making system: application to road traffic control. 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2016, San Jose, United States. pp.670-677, ⟨10.1109/ICTAI.2016.0107⟩. ⟨hal-01371774⟩



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