Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Online Access: | https://www.mdpi.com/2078-2489/14/8/441 |
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| author | Ismael T. Freire Xerxes D. Arsiwalla Jordi-Ysard Puigbò Paul Verschure |
| author_facet | Ismael T. Freire Xerxes D. Arsiwalla Jordi-Ysard Puigbò Paul Verschure |
| author_sort | Ismael T. Freire |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T23:51:55Z |
| format | Article |
| id | doaj.art-e79f538d91ef4d61a28d9ad5493ec447 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T23:51:55Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-e79f538d91ef4d61a28d9ad5493ec4472023-11-19T01:34:43ZengMDPI AGInformation2078-24892023-08-0114844110.3390/info14080441Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback ControlIsmael T. Freire0Xerxes D. Arsiwalla1Jordi-Ysard Puigbò2Paul Verschure3Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 AJ Nijmegen, The NetherlandsDepartment of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, SpainDepartment of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, SpainDonders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 AJ Nijmegen, The NetherlandsA 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.https://www.mdpi.com/2078-2489/14/8/441theory of mindmulti-agent systemsgame theorycognitive architecturesreinforcement learning |
| spellingShingle | Ismael T. Freire Xerxes D. Arsiwalla Jordi-Ysard Puigbò Paul Verschure Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control Information theory of mind multi-agent systems game theory cognitive architectures reinforcement learning |
| title | Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control |
| title_full | Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control |
| title_fullStr | Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control |
| title_full_unstemmed | Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control |
| title_short | Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control |
| title_sort | modeling theory of mind in dyadic games using adaptive feedback control |
| topic | theory of mind multi-agent systems game theory cognitive architectures reinforcement learning |
| url | https://www.mdpi.com/2078-2489/14/8/441 |
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