| Modelling Agent Policies with Interpretable Imitation Learning T Bewley, J Lawry, A Richards Trustworthy AI - Integrating Learning, Optimization and Reasoning, 180-186, 2021 | 6 | 2021 |
| TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments T Bewley, J Lawry AAAI Conference on Artificial Intelligence, 2021 | 6 | 2021 |
| On the combination of gamification and crowd computation in industrial automation and robotics applications T Bewley, M Liarokapis 2019 International Conference on Robotics and Automation (ICRA), 1955-1961, 2019 | 5 | 2019 |
| On Tour: Harnessing Social Tourism Data for City and Point of Interest Recommendation T Bewley, I Palomares Carrascosa 1st International ‘Alan Turing’ Conference on Decision Support and …, 2019 | 2 | 2019 |
| Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions T Bewley, F Lecue arXiv preprint arXiv:2112.11230, 2021 | 1 | 2021 |
| Am I Building a White Box Agent or Interpreting a Black Box Agent? T Bewley arXiv preprint arXiv:2007.01187, 2020 | 1 | 2020 |
| Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning J Early, T Bewley, C Evers, S Ramchurn arXiv preprint arXiv:2205.15367, 2022 | | 2022 |
| Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction T Bewley, J Lawry, A Richards arXiv preprint arXiv:2201.07749, 2022 | | 2022 |
Jonathan LawryProfessor of Artificial Intelligence, University of BristolVerified email at bris.ac.uk
