Modeling theory of mind in dyadic games using adaptive feedback control
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- dc.contributor.author Freire, Ismael T.
- dc.contributor.author Arsiwalla, Xerxes D.
- dc.contributor.author Puigbò Llobet, Jordi-Ysard
- dc.contributor.author Verschure, Paul F. M. J.
- dc.date.accessioned 2024-06-25T06:04:48Z
- dc.date.available 2024-06-25T06:04:48Z
- dc.date.issued 2023
- dc.description.abstract A major challenge in cognitive science and AI has been to understand how intelligent autonomous agents might acquire and predict the behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals, beliefs, and actions of other agents it interacts with? What are the computational principles to model a Theory of Mind (ToM)? Deep learning approaches to address these questions fall short of a better understanding of the problem. In part, this is due to the black-box nature of deep networks, wherein computational mechanisms of ToM are not readily revealed. Here, we consider alternative hypotheses seeking to model how the brain might realize a ToM. In particular, we propose embodied and situated agent models based on distributed adaptive control theory to predict the actions of other agents in five different game-theoretic tasks (Harmony Game, Hawk-Dove, Stag Hunt, Prisoner’s Dilemma, and Battle of the Exes). Our multi-layer control models implement top-down predictions from adaptive to reactive layers of control and bottom-up error feedback from reactive to adaptive layers. We test cooperative and competitive strategies among seven different agent models (cooperative, greedy, tit-for-tat, reinforcement-based, rational, predictive, and internal agents). We show that, compared to pure reinforcement-based strategies, probabilistic learning agents modeled on rational, predictive, and internal phenotypes perform better in game-theoretic metrics across tasks. The outlined autonomous multi-agent models might capture systems-level processes underlying a ToM and suggest architectural principles of ToM from a control-theoretic perspective.
- dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement ID:820742, and from the European Union’s Horizon EIC Grants 2021 under grant agreement ID:101071178.
- dc.format.mimetype application/pdf
- dc.identifier.citation Freire IT, Arsiwalla XD, Puigbò JY, Verschure PFMJ. Modeling theory of mind in dyadic games using adaptive feedback control. Information. 2023 Aug 4;14(8):441. DOI: 10.3390/info14080441
- dc.identifier.doi http://dx.doi.org/10.3390/info14080441
- dc.identifier.issn 2078-2489
- dc.identifier.uri http://hdl.handle.net/10230/60562
- dc.language.iso eng
- dc.publisher MDPI
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/820742
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101071178
- dc.rights © 2023 por los autores. Licenciatario MDPI, Basilea, Suiza. Este artículo es un artículo de acceso abierto distribuido bajo los términos y condiciones de la licencia Creative Commons Attribution (CC BY) (https://creativecommons.org/licenses/by/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Theory of mind
- dc.subject.keyword Multi-agent systems
- dc.subject.keyword Game theory
- dc.subject.keyword Cognitive architectures
- dc.subject.keyword Reinforcement learning
- dc.title Modeling theory of mind in dyadic games using adaptive feedback control
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion