Neuro-sybolic theorem proving
Primary supervisor
Additional information
- Machine Learning Meets The Herbrand Universe J. Piepenbrock, J. Urban, K. Korovin, M. Olsak, T. Heskes, M. Janota
- Premise selection with neural networks and distributed representation of features A. S. Kucik, K. Korovin,
Contact admissions office
Other projects with the same supervisor
- Solving non-linear constraints over continuous functions
- Solving mathematical problems using automated theorem provers
- Optimization and verification of systems modelled using neural networks
- Software verification with contrained Horn clauses and first-order theorem provers
- Symmetries and Automated Theorem Proving
Funding
- Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.
Project description
There are two major paradigms in AI: machine learning and symbolic reasoning. Symbolic reasoning has many applications ranging from theorem proving in mathematics to verification of hardware and software. On the other side, machine learning is highly effective in recognising complicated patterns in data.
This project is focused on combining both worlds: efficient deductive symbolic reasoning with guidance based on machine learning. The reasoning part will be done by highly efficient calculi for first-order logic such as superposition and instantiation, whereas machine learning using reinforcement learning, transformers and graph neural networks.
The developed methods will be integrated into a state-of-the-art theorem prover iProver.
There are many research challenges, solving which can have high impact in many applications.
Person specification
For information
- Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
- Candidates must meet the School's minimum English Language requirement.
- Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
Essential
Applicants will be required to evidence the following skills and qualifications.
- This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
- You must be capable of performing at a very high level.
- You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.
Desirable
Applicants will be required to evidence the following skills and qualifications.
- You will have good time management.
- You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
General
Applicants will be required to address the following.
- Comment on your transcript/predicted degree marks, outlining both strong and weak points.
- Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
- How well does your previous study prepare you for undertaking Postgraduate Research?
- Why do you believe you are suitable for doing Postgraduate Research?