# An intelligent tutor for planning in large partially observable environments

arXiv: https://arxiv.org/abs/2302.02785 · HTML: https://mlsanity.com/paper/2302.02785/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 69 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Write, Execute, Assess: Program Synthesis with a REPL](https://mlsanity.com/paper/1906.04604/) (2019)
- [Learning to select computations](https://mlsanity.com/paper/1711.06892/) (2017)
- [Automatic Discovery of Interpretable Planning Strategies](https://mlsanity.com/paper/2005.11730/) (2020)
- [Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning](https://mlsanity.com/paper/2102.00521/) (2021)
- [Boosting human decision-making with AI-generated decision aids](https://mlsanity.com/paper/2203.02776/) (2022)

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

- [Discovering the curriculum with AI: A proof-of-concept demonstration with an intelligent tutoring system for teaching project selection](https://mlsanity.com/paper/2406.04082/) (2024)

## Abstract (arXiv)

AI can not only outperform people in many planning tasks, but it can also teach them how to plan better. A recent and promising approach to improving human decision-making is to create intelligent tutors that utilize AI to discover and teach optimal planning strategies automatically. Prior work has shown that this approach can improve planning in artificial, fully observable planning tasks. Unlike these artificial tasks, many of the real-world situations in which people have to make plans include features that are only partially observable. To bridge this gap, we develop and evaluate the first intelligent tutor for planning in partially observable environments. Compared to previous intelligent tutors for teaching planning strategies, this novel intelligent tutor combines two innovations: 1) a new metareasoning algorithm for discovering optimal planning strategies for large, partially observable environments, and 2) scaffolding the learning process by having the learner choose from an increasing larger set of planning operations in increasingly larger planning problems. We found that our new strategy discovery algorithm is superior to the state-of-the-art. A preregistered experiment with 330 participants demonstrated that the new intelligent tutor is highly effective at improving people's ability to make good decisions in partially observable environments. This suggests our intelligent cognitive tutor can successfully boost human planning in complex, partially observable sequential decision problems. That makes the work presented in this a promising step towards using AI-powered intelligent tutors to improve human planning in the real world.