# Utility-based Adaptive Teaching Strategies using Bayesian Theory of Mind

arXiv: https://arxiv.org/abs/2309.17275 · HTML: https://mlsanity.com/paper/2309.17275/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning](https://mlsanity.com/paper/1910.08348/) (2019)
- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior](https://mlsanity.com/paper/1805.08010/) (2018)
- [Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications](https://mlsanity.com/paper/1805.07687/) (2018)
- [I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons](https://mlsanity.com/paper/2212.10060/) (2022)
- [Machine Teaching for Bayesian Learners in the Exponential Family](https://mlsanity.com/paper/1306.4947/) (2013)
- [Inferring the Goals of Communicating Agents from Actions and Instructions](https://mlsanity.com/paper/2306.16207/) (2023)
- [Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models](https://mlsanity.com/paper/2301.05149/) (2022)
- [Learning Theory of Mind via Dynamic Traits Attribution](https://mlsanity.com/paper/2204.09047/) (2022)
- [Towards Teachable Autotelic Agents](https://mlsanity.com/paper/2105.11977/) (2021)
- [Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments](https://mlsanity.com/paper/2206.04546/) (2022)

## Built on by (most cited first, 1 of 1)

- [Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes](https://mlsanity.com/paper/2506.13583/) (2025)

## Abstract (arXiv)

Good teachers always tailor their explanations to the learners. Cognitive scientists model this process under the rationality principle: teachers try to maximise the learner's utility while minimising teaching costs. To this end, human teachers seem to build mental models of the learner's internal state, a capacity known as Theory of Mind (ToM). Inspired by cognitive science, we build on Bayesian ToM mechanisms to design teacher agents that, like humans, tailor their teaching strategies to the learners. Our ToM-equipped teachers construct models of learners' internal states from observations and leverage them to select demonstrations that maximise the learners' rewards while minimising teaching costs. Our experiments in simulated environments demonstrate that learners taught this way are more efficient than those taught in a learner-agnostic way. This effect gets stronger when the teacher's model of the learner better aligns with the actual learner's state, either using a more accurate prior or after accumulating observations of the learner's behaviour. This work is a first step towards social machines that teach us and each other, see https://teacher-with-tom.github.io.