Bewley, Tom, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, and Manuela Veloso. "Counterfactual Metarules for Local and Global Recourse" International Conference on Machine Learning (ICML). 2024. [PDF]
Bewley, Tom, Jonathan Lawry, and Arthur Richards. "Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback" AIAA SciTech Forum. 2023. [PDF]
Early, Joseph, Tom Bewley, Christine Evers, and Sarvapali Ramchurn. "Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning" Proc. of the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022). 2022. [PDF]
Bewley, Tom and Lecue, Freddy. "Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions" Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022). 2022. [PDF]
Bewley, Tom and Lawry, Jonathan. "TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments" 35th AAAI Conference on Artificial Intelligence (AAAI 2021). 2021. [PDF]
Bewley, Tom, and Carrascosa, Ivan Palomares. "On Tour: Harnessing Social Tourism Data for City and Point of Interest Recommendation." 1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019). 2019. [PDF]
Bewley, Tom, and Liarokapis, Minas. "On the Combination of Gamification and Crowd Computation in Industrial Automation and Robotics Applications." 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. [PDF]
Jeen, Scott and Tom Bewley and Jonathan M. Cullen. "Conversative World Models" Seventh Workshop on Generalization in Planning. 2023. [PDF]
Bewley, Tom and Lawry, Jonathan and Richards, Arthur. "Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction" XAI-IJCAI22 Workshop. 2022. [PDF]
Bewley T., Lawry J., Richards A. (2021) Modelling Agent Policies with Interpretable Imitation Learning. In: Heintz F., Milano M., O'Sullivan B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science, vol 12641. Springer, Cham. [PDF]
Bewley, Tom, Jonathan Lawry, Arthur Richards, Rachel Craddock, and Ian Henderson. "Reward Learning with Trees: Methods and Evaluation" arXiv preprint 2210.01007. 2022. [PDF]
Bewley, Tom. "Am I Building a White Box Agent or Interpreting a Black Box Agent?" arXiv preprint 2007.01187. 2020. [PDF]
Bewley, Tom. "Tree Models for Interpretable Agents." PhD Thesis, University of Bristol, 2024. [PDF]
Bewley, Tom. "On Tour: Harnessing Social Tourism Data for City and Point-of-Interest Recommendation." MSc Thesis, University of Bristol, 2019. [PDF]
Bewley, Tom. "Use of Machine Vision and Intelligent Data Processing Algorithms to Monitor and Predict Crop Growth in Vertical Farms." BEng Thesis, University of Bristol, 2018. [PDF]