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A better-response strategy for self-interested planning agents. / Jordán, Jaume; Torreño, Alejandro; de Weerdt, Mathijs; Onaindia, Eva.

In: Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies, Vol. 48, No. 4, 2018, p. 1020-1040.

Research output: Scientific - peer-reviewArticle

Harvard

Jordán, J, Torreño, A, de Weerdt, M & Onaindia, E 2018, 'A better-response strategy for self-interested planning agents' Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies, vol 48, no. 4, pp. 1020-1040. DOI: 10.1007/s10489-017-1046-5

APA

Jordán, J., Torreño, A., de Weerdt, M., & Onaindia, E. (2018). A better-response strategy for self-interested planning agents. Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies, 48(4), 1020-1040. DOI: 10.1007/s10489-017-1046-5

Vancouver

Jordán J, Torreño A, de Weerdt M, Onaindia E. A better-response strategy for self-interested planning agents. Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies. 2018;48(4):1020-1040. Available from, DOI: 10.1007/s10489-017-1046-5

Author

Jordán, Jaume ; Torreño, Alejandro ; de Weerdt, Mathijs ; Onaindia, Eva. / A better-response strategy for self-interested planning agents. In: Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies. 2018 ; Vol. 48, No. 4. pp. 1020-1040

BibTeX

@article{c47ca69fad9d486397968aa915df86b9,
title = "A better-response strategy for self-interested planning agents",
abstract = "When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents’ strategic behavior considering the interactions as part of the agents’ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents’ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.",
keywords = "Best-response, Better-response, Game theory, Nash equilibrium, Planning",
author = "Jaume Jordán and Alejandro Torreño and {de Weerdt}, Mathijs and Eva Onaindia",
year = "2018",
doi = "10.1007/s10489-017-1046-5",
volume = "48",
pages = "1020--1040",
journal = "Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies",
issn = "0924-669X",
publisher = "Springer Netherlands",
number = "4",

}

RIS

TY - JOUR

T1 - A better-response strategy for self-interested planning agents

AU - Jordán,Jaume

AU - Torreño,Alejandro

AU - de Weerdt,Mathijs

AU - Onaindia,Eva

PY - 2018

Y1 - 2018

N2 - When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents’ strategic behavior considering the interactions as part of the agents’ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents’ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.

AB - When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents’ strategic behavior considering the interactions as part of the agents’ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents’ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.

KW - Best-response

KW - Better-response

KW - Game theory

KW - Nash equilibrium

KW - Planning

UR - http://www.scopus.com/inward/record.url?scp=85028538376&partnerID=8YFLogxK

UR - http://resolver.tudelft.nl/uuid:c47ca69f-ad9d-4863-9796-8aa915df86b9

U2 - 10.1007/s10489-017-1046-5

DO - 10.1007/s10489-017-1046-5

M3 - Article

VL - 48

SP - 1020

EP - 1040

JO - Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies

T2 - Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies

JF - Applied Intelligence: the international journal of artificial intelligence, neural networks, and complex problem-solving technologies

SN - 0924-669X

IS - 4

ER -

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