Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs

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Date
2022
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Language
en
Pages
9
235-243
Series
International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, Volume 1
Abstract
Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human and have the means to help them. We present a novel formulation of the interaction between the human and the AI as a sequential game where the agents are modelled using Bayesian best-response models. We show that in this case the AI's problem of helping bounded-rational humans make better decisions reduces to a Bayes-adaptive POMDP. In our simulated experiments, we consider an instantiation of our framework for humans who are subjectively optimistic about the AI's future behaviour. Our results show that when equipped with a model of the human, the AI can infer the human's bounds and nudge them towards better decisions. We discuss ways in which the machine can learn to improve upon its own limitations as well with the help of the human. We identify a novel trade-off for centaurs in partially observable tasks: for the AI's actions to be acceptable to the human, the machine must make sure their beliefs are sufficiently aligned, but aligning beliefs might be costly. We present a preliminary theoretical analysis of this trade-off and its dependence on task structure.
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Funding Information: This work was supported by: the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence; decision 828400), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 758824 -INFLUENCE), the UKRI Turing AI World-Leading Researcher Fellowship EP/W002973/1, ELISE travel grant (GA no 951847), KAUTE Foundation, and the Aalto Science-IT Project. Funding Information: This work was supported by: the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence; decision 828400), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 758824 —INFLUENCE), the UKRI Turing AI World-Leading Researcher Fellowship EP/W002973/1, ELISE travel grant (GA no 951847), KAUTE Foundation, and the Aalto Science-IT Project. Publisher Copyright: © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
Keywords
Bayesian Reinforcement Learning, Computational Rationality, Hybrid Intelligence, Multiagent Learning
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Citation
Çelikok , M M , Oliehoek , F A & Kaski , S 2022 , Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs . in International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 . Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS , vol. 1 , International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) , pp. 235-243 , International Conference on Autonomous Agents and Multiagent Systems , Auckland , New Zealand , 09/05/2022 . < https://arxiv.org/abs/2204.01160 >