Computing and Information Systems - Theses

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Author: Mathugama Babun Appuhamilage, Prashan Madumal × End date: 2024 × Start date: 2020 × Subject: Artificial Intelligence ×

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    Explainable Reinforcement Learning Through a Causal Lens
    Mathugama Babun Appuhamilage, Prashan Madumal ( 2021)
    This thesis investigates methods for explaining and understanding how and why reinforcement learning agents select actions, from a causal perspective. Understanding the behaviours, decisions and actions exhibited by artificially intelligent agents has been a central theme of interest since the inception of agent research. As systems grow in complexity, the agents' underlying reasoning mechanisms can become opaque and the intelligibility towards humans can be diminished, which can have negative consequences in high-stakes and highly-collaborative domains. The explainable agency of an autonomous agent can aid in transferring the knowledge of this reasoning process to the user to improve intelligibility. If we are to build effective explainable agency, a careful inspection of how humans generate, select and communicate explanations is needed. Explaining the behaviour and actions of sequential decision making reinforcement learning (RL) agents introduces challenges such as handling long-term goals and rewards, in contrast to one-shot explanations in which the attention of explainability literature has largely focused. Taking inspirations from cognitive science and philosophy literature on the nature of explanation, this thesis presents a novel explainable model ---action influence models--- that can generate causal explanations for reinforcement learning agents. A human-centred approach is followed to extend action influence models to handle distal explanations of actions, i.e. explanations that present future causal dependencies. To facilitate an end-to-end explainable agency, an action influence discovery algorithm is proposed to learn the structure of the causal relationships from the RL agent's interactions. Further, a dialogue model is also introduced, that can instantiate the interactions of an explanation dialogue. The original work presented in this thesis reveals how a causal and human-centred approach can bring forth a strong explainable agency in RL agents.