Online Bayesian Learning of Agent Behavior in Differential Games
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909984940359680 |
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| author | Bianchin, Francesco Lefringhausen, Robert Hirche, Sandra |
| author_facet | Bianchin, Francesco Lefringhausen, Robert Hirche, Sandra |
| contents | This work introduces an online Bayesian game-theoretic method for behavior identification in multi-agent dynamical systems. By casting Hamilton-Jacobi-Bellman optimality conditions as linear-in-parameter residuals, the method enables fast sequential Bayesian updates, uncertainty-aware inference, and robust prediction from limited, noisy data-without history stacks. The approach accommodates nonlinear dynamics and nonquadratic value functions through basis expansions, providing flexible models. Experiments, including linear-quadratic and nonlinear shared-control scenarios, demonstrate accurate prediction with quantified uncertainty, highlighting the method's relevance for adaptive interaction and real-time decision making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05087 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Online Bayesian Learning of Agent Behavior in Differential Games Bianchin, Francesco Lefringhausen, Robert Hirche, Sandra Systems and Control This work introduces an online Bayesian game-theoretic method for behavior identification in multi-agent dynamical systems. By casting Hamilton-Jacobi-Bellman optimality conditions as linear-in-parameter residuals, the method enables fast sequential Bayesian updates, uncertainty-aware inference, and robust prediction from limited, noisy data-without history stacks. The approach accommodates nonlinear dynamics and nonquadratic value functions through basis expansions, providing flexible models. Experiments, including linear-quadratic and nonlinear shared-control scenarios, demonstrate accurate prediction with quantified uncertainty, highlighting the method's relevance for adaptive interaction and real-time decision making. |
| title | Online Bayesian Learning of Agent Behavior in Differential Games |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.05087 |