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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About
About me
Posts
blog
Start Here
Published:
Current Views on Philosophy and Ethics
Published:
TOM: The Omni-Maestro
Published:
portfolio
Assorted University Projects
Assessed work from BEng and MSc degrees
MSc Project: On Tour
Harnessing Social Data for Travel Recommendation
Minimal Hanabi Emulator
A minimal Hanabi emulator, written in pure Python.
publications
Use of Machine Vision and Intelligent Data Processing Algorithms to Monitor and Predict Crop Growth in Vertical Farms
Published in , 2018
Exerpt.
Recommended citation: 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. https://bit.ly/2MHrhWb
On the Combination of Gamiļ¬cation and Crowd Computation in Industrial Automation and Robotics Applications
Published in 2019 International Conference on Robotics and Automation (ICRA), 2019
A theoretical framework for embedding robotics problems into video games, enabling distributed control by a crowd of players.
Recommended citation: 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. /files/gamification.pdf
On Tour: Harnessing Social Tourism Data for City and Point-of-Interest Recommendation
Published in , 2019
Exerpt.
Recommended citation: Bewley, Tom. "On Tour: Harnessing Social Tourism Data for City and Point-of-Interest Recommendation." MSc Thesis, University of Bristol, 2019. /files/msc-thesis.pdf
On Tour: Harnessing Social Tourism Data for City and Point of Interest Recommendation
Published in DSRS-Turing 2019: 1st International āAlan Turingā Conference on Decision Support and Recommender Systems, 2019
A data-driven recommender system for tourism.
Recommended citation: Bewley, Tom, and Carrascosa, Ivan Palomares. "On Tour: Harnessing Social Tourism Data for City and Point of Interest Recommendation." 1st International āAlan Turingā Conference on Decision Support and Recommender Systems (DSRS-Turing 2019). 2019. https://www.researchgate.net/profile/Tom_Bewley2/publication/338581229_On_Tour_Harnessing_Social_Tourism_Data_for_City_and_Point_of_Interest_Recommendation/links/5e1dd8e6458515d2b46d3eb6/On-Tour-Harnessing-Social-Tourism-Data-for-City-and-Point-of-Interest-Recommendation.pdf
Am I Building a White Box Agent or Interpreting a Black Box Agent?
Published in arXiv, 2020
Describing the various ways of training and evaluating white box approximations of black box policies, and how these can be in conflict.
Recommended citation: Bewley, Tom. "Am I Building a White Box Agent or Interpreting a Black Box Agent?" arXiv preprint 2007.01187. 2020. https://arxiv.org/abs/2007.01187
Modelling Agent Policies with Interpretable Imitation Learning
Published in Trustworthy AI - Integrating Learning, Optimization and Reasoning (also 1st TAILOR Workshop at ECAI 2020), 2020
Introducing I2L and preliminary results.
Recommended citation: 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. https://link.springer.com/chapter/10.1007%2F978-3-030-73959-1_16
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments
Published in 35th AAAI Conference on Artificial Intelligence (AAAI 2021), 2020
Introducing a new decision tree model of black box agent behaviour, which jointly captures the policy, value function and temporal dynamics.
Recommended citation: 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. https://arxiv.org/abs/2009.04743
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions
Published in 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), 2021
An online, active learning algorithm that uses human preferences to construct reward functions with intrinsically interpretable, compositional tree structures.
Recommended citation: 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. http://arxiv.org/abs/2112.11230
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
Published in Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022
We generalise reward modelling for reinforcement learning to handle non-Markovian rewards, and propose new interpretable multiple instance learning models for this problem.
Recommended citation: 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. https://arxiv.org/abs/2205.15367
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction
Published in XAI-IJCAI22 Workshop, 2022
A data-driven, model-agnostic technique for generating a human-interpretable summary of the salient points of contrast within an evolving dynamical system.
Recommended citation: Bewley, Tom and Lawry, Jonathan and Richards, Arthur. "Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction" XAI-IJCAI22 Workshop. 2022. https://arxiv.org/abs/2201.07749
Reward Learning with Trees: Methods and Evaluation
Published in arXiv, 2022
We show that reward learning with tree models can be competitive with neural networks, and demonstrate some of its interpretability benefits.
Recommended citation: Bewley, Tom, Jonathan Lawry, Arthur Richards, Rachel Craddock, and Ian Henderson. "Reward Learning with Trees: Methods and Evaluation" arXiv preprint 2210.01007. 2022. https://arxiv.org/abs/2210.01007
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback
Published in AIAA SciTech Forum, 2023
We show that reward learning with tree models can be competitive with neural networks in an aircraft handling domain, and demonstrate some of its interpretability benefits.
Recommended citation: Bewley, Tom, Jonathan Lawry, and Arthur Richards. "Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback" AIAA SciTech Forum. 2023. https://arxiv.org/abs/2305.16924
readings
Weekly Readings #1
Published:
As it stands Iām precisely 13 days into my PhD, which means a lot of reading, and I thought Iād kick this blog off with a weekly rolling ādiaryā of things I read, watch and otherwise consume which may have some influence on my PhD topic. Most of the papers have words pertaining to explanation in there, and thatās because I did a massive scrape of papers with that keyword. I figured that would be a reasonable start.
Weekly Readings #2
Published:
Approximately three weeks in, Iām starting to work on a case study project that will allow me to explore some of the key ideas around multi-agent explainability ā collision avoidance within a population of autonomous vehicles on road / track networks. As a result, more of my reading this week has focused specifically on the multi-agent context.
Weekly Readings #3
Published:
This week didnāt involve very much reading since I focused instead on my practical investigation of the traffic coordination problem. Nonetheless, I encountered a variety of fascinating ideas.
Weekly Readings #4
Published:
Decision trees for state space segmentation; lightweight manual labelling as a āseedā for interpretability; the dangerous of homogenous distributed control; AI and the climate crisis.
Weekly Readings #5
Published:
The theory of why-questions; fidelity versus accuracy; trees and programs as RL policies; partially-interpretable hybrids.
Weekly Readings #6
Published:
Modelling other agents; DAGGER; evaluating feature importance visualisations; self, soul and circular ethics.
Weekly Readings #7
Published:
Meta learning causal relations; decomposing explanation questions; misleading explanations; the critical influence of metrics.
Weekly Readings #8
Published:
State representation learning; emotions and qualitative regions for heuristic explanation; causal reasoning as a middle ground between statistics and mechanics; deep learning and neuroscientific discovery.
Weekly Readings #9
Published:
Model extraction; world models and representations; a MAS taxonomy.
Weekly Readings #10
Published:
State representation learning in Atari; AI shortcuts and ethical debt; cloning swarms.
Weekly Readings #11
Published:
Distillation and cloning; onboard swarm evolution; The Mindās I chapters.
Weekly Readings #12
Published:
Goal hierarchies as rule sets; mutual information and auxiliary tasks for representation learning; model-based understanding.
Weekly Readings #13
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Theory-of-mind as a general solution; factual and counterfactual explanation; semantic development in neural networks; cloning without action knowledge; intuition pumps.
Weekly Readings #14
Published:
Integrating knowledge and machine learning; folk psychology and intentionality; soft decision trees; conceptual spaces.
Weekly Readings #15
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Confident execution framework; explananda as differences; online decision tree induction; hybrid AI design patterns.
Weekly Readings #16
Published:
Imitation by coaching; GAIL; human-centric vs robot-centric; DeepMimic.
Weekly Readings #17
Published:
Using $Q$ for imitation; differentiable decision trees and their application to RL; interactive explanations with Glass-Box.
Weekly Readings #18
Published:
Formalising interpretation and explanation; operationally-meaningful representations; Conceptual Spaces book.
Weekly Readings #19
Published:
Symbols and cognition; robust AI through hybridisation; causal modelling via RL interventions; environment as an engineered system.
Weekly Readings #20
Published:
Rule-based regularisation; DeepSHAP for augmenting GAN training; image schemas as conceptual primitives; imitating DDPG with a fuzzy rule-based system.
Weekly Readings #21
Published:
Constraining embeddings with side information; latent actions; RL with abstract representations and models; fuzzy state prototypes; index-free imitation.
Weekly Readings #22
Published:
Explanation-based tuning; saliency maps for vision-based policies; RL with differentiable decision trees.
Weekly Readings #23
Published:
Explanatory debugging; latent canonicalisations; the perceptual user interface; automatic curriculum learning.
Weekly Readings #24
Published:
Trustworthy AI; unifying imitation and policy gradient; soft decision trees; SRL with dimension specialisation.
Weekly Readings #25
Published:
Imitation learning using value or reward.
Weekly Readings #26
Published:
Terminological quagmires; (mis)interpreting interpolation; interaction is paramount.
talks
Scary Black Boxes: Why Explanation Lies at the Heart of Socially-responsible AI
Published:
PechaKucha talk introducing the motivation behind my PhD topic, as part of a one-day conference on socially-responsible AI.
On Tour: Harnessing Social Tourism Data for City and Point of Interest Recommendation
Published:
Talk presenting an overview of my MSc thesis project, as part of the 1st International āAlan Turingā conference on Decision Support and Recommender Systems.
Explainable AI for Black Box Autonomous Agents
Published:
Talk presenting an overview of my PhD research so far, as part of a one-day conference showcasing student work within the School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths (SCEEM) at the University of Bristol.
Modelling Agent Policies with Interpretable Imitation Learning
Published:
Presentation of my paper of the same name, which can be found here.
Towards Explanatory Interactive Reinforcement Learning for Aligned and Trustworthy Agents
Published:
Recording of a talk delivered to fellow Bristol researchers on 18/01/21, as part of a one-day symposium on Combining Knowledge and Data.
teaching
Engineering Design: Design Project 2
Undergraduate course, Faculty of Engineering, University of Bristol, 2019
Technical assistance for second year concept generation and MATLAB modelling project.
Engineering Design: Design Project 4
Undergraduate course, Faculty of Engineering, University of Bristol, 2019
Technical assistance for fourth year research and modelling activities, which form the first half of the centrepiece two-year group design project in the latter half of the Engineering Design programme.