IDEAS home Printed from https://ideas.repec.org/a/gam/jgames/v14y2023i3p36-d1135043.html
   My bibliography  Save this article

Improving Strategic Decisions in Sequential Games by Exploiting Positional Similarity

Author

Listed:
  • Sabrina Evans

    (Department of Mathematics, Yale University, New Haven, CT 06511, USA
    Bloop AI, London WC1H 9SE, UK)

  • Paolo Turrini

    (Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK)

Abstract

We study the strategic similarity of game positions in two-player extensive games of perfect information by looking at the structure of their local game trees, with the aim of improving the performance of game-playing agents in detecting forcing continuations. We present a range of measures over the induced game trees and compare them against benchmark problems in chess, observing a promising level of accuracy in matching up trap states. Our results can be applied to chess-like interactions where forcing moves play a role, such as those arising in bargaining and negotiation.

Suggested Citation

  • Sabrina Evans & Paolo Turrini, 2023. "Improving Strategic Decisions in Sequential Games by Exploiting Positional Similarity," Games, MDPI, vol. 14(3), pages 1-13, April.
    • Handle: RePEc:gam:jgames:v:14:y:2023:i:3:p:36-:d:1135043
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-4336/14/3/36/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-4336/14/3/36/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schwalbe, Ulrich & Walker, Paul, 2001. "Zermelo and the Early History of Game Theory," Games and Economic Behavior, Elsevier, vol. 34(1), pages 123-137, January.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    3. Ariel Rubinstein, 1997. "Modeling Bounded Rationality," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262681005, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guan, Xiaoshu & Sun, Huabin & Hou, Rongrong & Xu, Yang & Bao, Yuequan & Li, Hui, 2023. "A deep reinforcement learning method for structural dominant failure modes searching based on self-play strategy," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Xiaoyue Li & John M. Mulvey, 2023. "Optimal Portfolio Execution in a Regime-switching Market with Non-linear Impact Costs: Combining Dynamic Program and Neural Network," Papers 2306.08809, arXiv.org.
    3. Andrew Kosenko, 2020. "Mediated Persuasion," Papers 2012.00098, arXiv.org, revised Dec 2020.
    4. Nathan Companez & Aldeida Aleti, 2016. "Can Monte-Carlo Tree Search learn to sacrifice?," Journal of Heuristics, Springer, vol. 22(6), pages 783-813, December.
    5. Zhewei Zhang & Youngjin Yoo & Kalle Lyytinen & Aron Lindberg, 2021. "The Unknowability of Autonomous Tools and the Liminal Experience of Their Use," Information Systems Research, INFORMS, vol. 32(4), pages 1192-1213, December.
    6. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    7. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    8. Li Xia, 2020. "Risk‐Sensitive Markov Decision Processes with Combined Metrics of Mean and Variance," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2808-2827, December.
    9. Song-Kyoo (Amang) Kim, 2020. "A Versatile Stochastic Duel Game," Mathematics, MDPI, vol. 8(5), pages 1-13, May.
    10. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    11. Hosseini, Hamid, 2003. "The arrival of behavioral economics: from Michigan, or the Carnegie School in the 1950s and the early 1960s?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 32(4), pages 391-409, September.
    12. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    13. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    14. Burka, Dávid & Puppe, Clemens & Szepesváry, László & Tasnádi, Attila, 2022. "Voting: A machine learning approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1003-1017.
    15. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    16. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    17. Zhimian Chen & Yizeng Wang & Hao Hu & Zhipeng Zhang & Chengwei Zhang & Shukun Zhou, 2024. "Investigating Autonomous Vehicle Driving Strategies in Highway Ramp Merging Zones," Mathematics, MDPI, vol. 12(23), pages 1-22, December.
    18. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    19. Casilda Lasso de la Vega & Oscar Volij, 2020. "The value of a draw," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 70(4), pages 1023-1044, November.
    20. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jgames:v:14:y:2023:i:3:p:36-:d:1135043. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.