Matthew Morris
Matthew Morris
Wolfson Building, Parks Road, Oxford OX1 3QD
Interests
My research interests are broadly within the field of neuro-symbolic artificial intelligence, with a particular focus on neuro-symbolic logic models. However, I also have a history of working with both pure logic and reinforcement learning.
Biography
I did my undergraduate degree at the University of Cape Town in Mathematics and Computer Science, followed by a Master’s degree at Oxford in Computer Science. I have received numerous academic awards, was a Harry Crossley fellow and a Skye Foundation Scholar, and am currently funded by the EPSRC. I am currently doing my DPhil (PhD) in Computer Science at Oxford, supervised by Ian Horrocks, and am focusing on neuro-symbolic logic models. My general area of interest is anything neuro-symbolic. In the course of my career, I have worked as a software developer for Amazon Web Services, a research assistant, and most recently as a research engineer for InstaDeep, where I worked on multi-agent reinforcement learning research. I have several publications in the areas of machine learning, logic, and reinforcement learning, most recently in NeurIPS where I used Graph Neural Networks for communication in multi-agent reinforcement learning. If you want to play some ultimate frisbee, challenge me to a chess match, or chat about my research, please send me an email.
Selected Publications
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Universally Expressive Communication in Multi−Agent Reinforcement Learning
Matthew Morris Thomas D Barrett and Arnu Pretorius
In Thirty−sixth Conference on Neural Information Processing Systems (NeurIPS). 2022.
Details about Universally Expressive Communication in Multi−Agent Reinforcement Learning | BibTeX data for Universally Expressive Communication in Multi−Agent Reinforcement Learning | Link to Universally Expressive Communication in Multi−Agent Reinforcement Learning
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Learning Proof Path Selection Policies in Neural Theorem Proving
Matthew Morris‚ Pasquale Minervini and Phil Blunsom
2022.
Details about Learning Proof Path Selection Policies in Neural Theorem Proving | BibTeX data for Learning Proof Path Selection Policies in Neural Theorem Proving | Download (pdf) of Learning Proof Path Selection Policies in Neural Theorem Proving
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Algorithmic definitions for KLM−style defeasible disjunctive Datalog
Matthew Morris‚ Tala Ross and Thomas Meyer
In South African Computer Journal. Vol. 32. Pages 141−160. 2020.
Details about Algorithmic definitions for KLM−style defeasible disjunctive Datalog | BibTeX data for Algorithmic definitions for KLM−style defeasible disjunctive Datalog | DOI (https://doi.org/10.18489/sacj.v32i2.846)