# Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)

arXiv: https://arxiv.org/abs/2306.04806 · HTML: https://mlsanity.com/paper/2306.04806/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Visualizing and Understanding Atari Agents](https://mlsanity.com/paper/1711.00138/) (2017)
- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [Data-efficient Deep Reinforcement Learning for Dexterous Manipulation](https://mlsanity.com/paper/1704.03073/) (2017)
- [Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations](https://mlsanity.com/paper/2002.01080/) (2020)
- [Expected Value of Communication for Planning in Ad Hoc Teamwork](https://mlsanity.com/paper/2103.01171/) (2021)
- [Learning Interpretable Concept-Based Models with Human Feedback](https://mlsanity.com/paper/2012.02898/) (2020)
- [Anytime Integrated Task and Motion Policies for Stochastic Environments](https://mlsanity.com/paper/1904.13006/) (2019)
- [Discovering User-Interpretable Capabilities of Black-Box Planning Agents](https://mlsanity.com/paper/2107.13668/) (2021)
- [Differential Assessment of Black-Box AI Agents](https://mlsanity.com/paper/2203.13236/) (2022)

## Built on by (most cited first, 6 of 6)

- [Assessing the Impact of Distribution Shift on Reinforcement Learning Performance](https://mlsanity.com/paper/2402.03590/) (2024)
- [Efficient Evaluation of Multi-Task Robot Policies With Active Experiment Selection](https://mlsanity.com/paper/2502.09829/) (2025)
- [Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings](https://mlsanity.com/paper/2402.08145/) (2024)
- [Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks](https://mlsanity.com/paper/2502.13006/) (2025)
- [Autonomous Assessment of Generalizability of AI Agent Capabilities](https://mlsanity.com/paper/2512.16733/) (2025)
- [RAMP: Hybrid DRL for Online Learning of Numeric Action Models](https://mlsanity.com/paper/2604.08685/) (2026)

## Abstract (arXiv)

It is essential for users to understand what their AI systems can and can't do in order to use them safely. However, the problem of enabling users to assess AI systems with sequential decision-making (SDM) capabilities is relatively understudied. This paper presents a new approach for modeling the capabilities of black-box AI systems that can plan and act, along with the possible effects and requirements for executing those capabilities in stochastic settings. We present an active-learning approach that can effectively interact with a black-box SDM system and learn an interpretable probabilistic model describing its capabilities. Theoretical analysis of the approach identifies the conditions under which the learning process is guaranteed to converge to the correct model of the agent; empirical evaluations on different agents and simulated scenarios show that this approach is few-shot generalizable and can effectively describe the capabilities of arbitrary black-box SDM agents in a sample-efficient manner.