TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments

Authors

  • Tom Bewley Department of Engineering Mathematics, University of Bristol
  • Jonathan Lawry Department of Engineering Mathematics, University of Bristol

Keywords:

Accountability, Interpretability & Explainability, User Experience and Usability, Reinforcement Learning

Abstract

In explainable artificial intelligence, there is increasing interest in understanding the behaviour of autonomous agents to build trust and validate performance. Modern agent architectures, such as those trained by deep reinforcement learning, are currently so lacking in interpretable structure as to effectively be black boxes, but insights may still be gained from an external, behaviourist perspective. Inspired by conceptual spaces theory, we suggest that a versatile first step towards general understanding is to discretise the state space into convex regions, jointly capturing similarities over the agent's action, value function and temporal dynamics within a dataset of observations. We create such a representation using a novel variant of the CART decision tree algorithm, and demonstrate how it facilitates practical understanding of black box agents through prediction, visualisation and rule-based explanation.

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Published

2021-05-18

How to Cite

Bewley, T., & Lawry, J. (2021). TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11415-11422. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17360

Issue

Section

AAAI Technical Track on Philosophy and Ethics of AI