# An Epistemic Human-Aware Task Planner which Anticipates Human Beliefs and Decisions

arXiv: https://arxiv.org/abs/2409.18545 · HTML: https://mlsanity.com/paper/2409.18545/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 27 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems](https://mlsanity.com/paper/2109.09904/) (2021)
- [A Hierarchical Architecture for Human-Robot Cooperation Processes](https://mlsanity.com/paper/2009.02807/) (2020)
- [Cooperative Epistemic Multi-Agent Planning for Implicit Coordination](https://mlsanity.com/paper/1703.02196/) (2017)
- [HATP: An HTN Planner for Robotics](https://mlsanity.com/paper/1405.5345/) (2014)

## Abstract (arXiv)

We present a substantial extension of our Human-Aware Task Planning framework, tailored for scenarios with intermittent shared execution experiences and significant belief divergence between humans and robots, particularly due to the uncontrollable nature of humans. Our objective is to build a robot policy that accounts for uncontrollable human behaviors, thus enabling the anticipation of possible advancements achieved by the robot when the execution is not shared, e.g. when humans are briefly absent from the shared environment to complete a subtask. But, this anticipation is considered from the perspective of humans who have access to an estimated model for the robot. To this end, we propose a novel planning framework and build a solver based on AND-OR search, which integrates knowledge reasoning, including situation assessment by perspective taking. Our approach dynamically models and manages the expansion and contraction of potential advances while precisely keeping track of when (and when not) agents share the task execution experience. The planner systematically assesses the situation and ignores worlds that it has reason to think are impossible for humans. Overall, our new solver can estimate the distinct beliefs of the human and the robot along potential courses of action, enabling the synthesis of plans where the robot selects the right moment for communication, i.e. informing, or replying to an inquiry, or defers ontic actions until the execution experiences can be shared. Preliminary experiments in two domains, one novel and one adapted, demonstrate the effectiveness of the framework.