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Authors: Yulia Korukhova and Sergey Kuryshev

Affiliation: M.V. Lomonosov Moscow State University, Russian Federation

ISBN: 978-989-758-219-6

Keyword(s): Multi-agent Systems, Neural Networks, Dominated Strategies.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Evolutionary Computing ; Formal Methods ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Multi-Agent Systems ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Planning and Scheduling ; Sensor Networks ; Signal Processing ; Simulation and Modeling ; Soft Computing ; Software Engineering ; Symbolic Systems ; Task Planning and Execution ; Theory and Methods

Abstract: The paper deals with multi-agent system that represents trading agents acting in the environment with imperfect information. Fictitious play algorithm, first proposed by Brown in 1951, is a popular theoretical model of training agents. However, it is not applicable to larger systems with imperfect information due to its computational complexity. In this paper we propose a modification of the algorithm. We use neural networks for fast approximate calculation of the best responses. An important feature of the algorithm is the absence of agent’s a priori knowledge about the system. Agents’ learning goes through trial and error with winning actions being reinforced and entered into the training set and losing actions being cut from the strategy. The proposed algorithm has been used in a small game with imperfect information. And the ability of the algorithm to remove iteratively dominated strategies of agents' behavior has been demonstrated.

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Paper citation in several formats:
Korukhova Y. and Kuryshev S. (2017). Training Agents with Neural Networks in Systems with Imperfect Information.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-219-6, pages 296-301. DOI: 10.5220/0006242102960301

@conference{icaart17,
author={Yulia Korukhova and Sergey Kuryshev},
title={Training Agents with Neural Networks in Systems with Imperfect Information},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2017},
pages={296-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006242102960301},
isbn={978-989-758-219-6},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Training Agents with Neural Networks in Systems with Imperfect Information
SN - 978-989-758-219-6
AU - Korukhova Y.
AU - Kuryshev S.
PY - 2017
SP - 296
EP - 301
DO - 10.5220/0006242102960301

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