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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. It has increasingly been applied in Human-AI and Human-Robot interactions, allowing artificial agents to recognize the goals
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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.
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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. It is a method commonly used in
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503:{\displaystyle P(a_{i}|g,s_{0})={\frac {\exp({\frac {1}{\beta }}Q(s_{0},a_{i}))}{\sum _{a_{j}}{\exp({\frac {1}{\beta }}Q(s_{0},a_{j})))}}}}
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In the forward planning model, it is often assumed that the agent is rational. The agents' actions can then be derived from a
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Wu, Sarah A.; Wang, Rose E.; Evans, James A.; Tenenbaum, Joshua B.; Parkes, David C.; Kleiman-Weiner, Max (2021-04-07).
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1061:"NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants"
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Zhi-Xuan, Tan; Mann, Jordyn L.; Silver, Tom; Tenenbaum, Joshua B.; Mansinghka, Vikash K. (2020-12-06).
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Pragmatic
Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
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to invert the conditional probability to find the posterior probability of the agent's goal.
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Shum, Michael; Kleiman-Weiner, Max; Littman, Michael L.; Tenenbaum, Joshua B. (2019-07-17).
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Baker, Chris L.; Jara-Ettinger, Julian; Saxe, Rebecca; Tenenbaum, Joshua B. (2017-03-13).
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1022:"Rational quantitative attribution of beliefs, desires and percepts in human mentalizing"
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Proceedings of the 34th
International Conference on Neural Information Processing Systems
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It may require cleanup to comply with
Knowledge (XXG)'s content policies, particularly
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Zhi-Xuan, Tan; Ying, Lance; Mansinghka, Vikash; Tenenbaum, Joshua B. (2024-02-27),
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Puig, Xavier; Shu, Tianmin; Tenenbaum, Joshua B.; Torralba, Antonio (2023-05-29).
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1132:"Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration"
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Ying, Lance; Zhi-Xuan, Tan; Mansinghka, Vikash; Tenenbaum, Joshua B. (2023).
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973:"Theory of Minds: Understanding Behavior in Groups through Inverse Planning"
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887:"Inferring the Goals of Communicating Agents from Actions and Instructions"
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163:, which attempts to learn a reward function based on agents' behavior, and
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167:, which finds logically-consistent goals given the action observations.
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936:"Online Bayesian goal inference for boundedly-rational planning agents"
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Baker, Chris L.; Saxe, Rebecca; Tenenbaum, Joshua B. (December 2009).
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Inverse
Planning is often framed with a Bayesian formulation, such as
1065:
2023 IEEE International
Conference on Robotics and Automation (ICRA)
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942:. NIPS'20. Red Hook, NY, USA: Curran Associates Inc.: 19238–19250.
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Proceedings of the Annual
Meeting of the Cognitive Science Society
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773:{\displaystyle P(g|a_{1:t},s_{0})\propto P(a_{1:t}|g,s_{0})P(g)}
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Proceedings of the AAAI Conference on
Artificial Intelligence
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Baker, Chris L.; Tenenbaum, J. B.; Saxe, Rebecca R. (2007).
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and beliefs of human users in order to provide assistance.
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A major contributor to this article appears to have a
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179:A causal Diagram of agent's goal and actions
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891:Proceedings of the AAAI Symposium Series
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864:"Goal Inference as Inverse Planning"
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1136:Topics in Cognitive Science
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547:{\displaystyle Q(s_{0},a)}
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