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 language | English |
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| Title of host publication | International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 235-243 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-1-7138-5433-3 |
| Publication status | Published - 2022 |
| MoE publication type | A4 Conference publication |
| Event | International Conference on Autonomous Agents and Multiagent Systems - Virtual, Online, Auckland, New Zealand Duration: 9 May 2022 → 13 May 2022 Conference number: 21 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Publisher | IFAAMAS |
| Volume | 1 |
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Conference
| Conference | International Conference on Autonomous Agents and Multiagent Systems |
|---|---|
| Abbreviated title | AAMAS |
| Country/Territory | New Zealand |
| City | Auckland |
| Period | 09/05/2022 → 13/05/2022 |
Funding
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. 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.
Keywords
- Bayesian Reinforcement Learning
- Computational Rationality
- Hybrid Intelligence
- Multiagent Learning