Draft:Inverse Planning
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Inverse Planning refers to the process of inferring an agent's mental states, such as goals, beliefs, emotions, etc., from actions by assuming agents are rational planners.
Inverse Planning is closely related to
Bayesian Inverse Planning
Inverse Planning is often framed with a Bayesian formulation, such as sequential Monte Carlo methods. The inference process can be represented with a graphical model shown on the right. In this causal diagram, a rational agent with a goal g produces a plan with a sequence of actions , where
In the forward planning model, it is often assumed that the agent is rational. The agents' actions can then be derived from a Boltzmann rational action distribution,
where is the cost of the optimal plan to goal by first performing action , and is the Boltzmann temperature parameter.
Then giving action observations of , Inverse Planning applies
Inverse planning can also be applied for inferring agent's beliefs, emotions, preferences, etc. Recent work in Bayesian Inverse Planning has also been able to account for boundedly rational agent behavior, multi-modal interactions, and team actions in multi-agent systems.[3][4][5]
Application
Inverse Planning has been widely used in modeling agent's behavior in cognitive science to understand human's ability to interpret and infer other agents' latent mental states.[1][2][6] It has increasingly been applied in Human-AI and Human-Robot interactions, allowing artificial agents to recognize the goals and beliefs of human users in order to provide assistance.[7][8][9]
References
- ^ PMID 19729154.
- ^ a b Baker, Chris L.; Tenenbaum, J. B.; Saxe, Rebecca R. (2007). "Goal Inference as Inverse Planning". Proceedings of the Annual Meeting of the Cognitive Science Society. 29 (29).
- ISSN 2994-4317.
- S2CID 219687443.
- ISSN 2374-3468.
- ISSN 2397-3374.
- ISBN 979-8-3503-2365-8.
- arXiv:2402.17930
- PMID 33829670.