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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.

Original languageEnglish
Title of host publicationInternational Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages235-243
Number of pages9
ISBN (Electronic)9781713854333
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Autonomous Agents and Multiagent Systems - Virtual, Online, Auckland, New Zealand
Duration: 9 May 202213 May 2022
Conference number: 21

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
PublisherIFAAMAS
Volume1
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems
Abbreviated titleAAMAS
Country/TerritoryNew Zealand
CityAuckland
Period09/05/202213/05/2022

Keywords

  • Bayesian Reinforcement Learning
  • Computational Rationality
  • Hybrid Intelligence
  • Multiagent Learning

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