# Axiom Learning and Belief Tracing for Transparent Decision Making in Robotics

arXiv: https://arxiv.org/abs/2010.10645 · HTML: https://mlsanity.com/paper/2010.10645/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach](https://mlsanity.com/paper/2402.19265/) (2024)
- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)
- [Combining Commonsense Reasoning and Knowledge Acquisition to Guide Deep Learning in Robotics](https://mlsanity.com/paper/2201.10266/) (2022)

## Abstract (arXiv)

A robot's ability to provide descriptions of its decisions and beliefs promotes effective collaboration with humans. Providing such transparency is particularly challenging in integrated robot systems that include knowledge-based reasoning methods and data-driven learning algorithms. Towards addressing this challenge, our architecture couples the complementary strengths of non-monotonic logical reasoning, deep learning, and decision-tree induction. During reasoning and learning, the architecture enables a robot to provide on-demand relational descriptions of its decisions, beliefs, and the outcomes of hypothetical actions. These capabilities are grounded and evaluated in the context of scene understanding tasks and planning tasks performed using simulated images and images from a physical robot manipulating tabletop objects.