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Details of Grant 

EPSRC Reference: EP/R019622/1
Title: Embedding Machine Learning within Quantifier Elimination Procedures
Principal Investigator: England, Dr M
Other Investigators:
Researcher Co-Investigators:
Project Partners:
CNRS Group Maplesoft RWTH Aachen University
University of Genoa
Department: Ctr for Fluid and Complex Systems
Organisation: Coventry University
Scheme: First Grant - Revised 2009
Starts: 01 July 2018 Ends: 31 December 2019 Value (£): 100,988
EPSRC Research Topic Classifications:
Artificial Intelligence Fundamentals of Computing
Software Engineering
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
27 Nov 2017 EPSRC ICT Prioritisation Panel Nov 2017 Announced
Summary on Grant Application Form
This project concerns computational mathematics and logic. The aim is to improve the ability of computers to perform ``Quantifier Elimination'' (QE).

We say a logical statement is ``quantified'' if it is preceded by a qualification such as "for all" or "there exists". Here is an example of a quantified statement:

"there exists x such that ax^2 + bx + c = 0 has two solutions for x".

While the statement is mathematically precise the implications are unclear - what restrictions does this statement of existence force upon us? QE corresponds to replacing a quantified statement by an unquantified one which is equivalent. In this case we may replace the statement by:

"b^2 - 4ac > 0", which is the condition for x to have two solutions.

You may have recognised this equivalence from GCSE mathematics, when studying the quadratic equation. The important point here is that the latter statement can actually be derived automatically by a computer from the former, using a QE procedure.

QE is not subject to the numerical rounding errors of most computations. Solutions are not in the form of a numerical answer but an algebraic description which offers insight into the structure of the problem at hand. In the example above, QE shows us not what the solutions to a particular quadratic equation are, but how in general the number of solutions depends on the coefficients a, b, and c.

QE has numerous applications throughout engineering and the sciences. An example from biology is the determination of medically important values of parameters in a biological network; while another from economics is identifying which hypotheses in economic theories are compatible, and for what values of the variables. In both cases, QE can theoretically help, but in practice the size of the statements means state-of-the-art procedures run out of computer time/memory.

The extensive development of QE procedures means they have many options and choices about how they are run. These decisions can greatly affect how long QE takes, rendering an intractable problem easy and vice versa. Making the right choice is a critical, but understudied problem and is the focus of this project. At the moment QE procedures make such choices either under direct supervision of a human or based on crude human-made heuristics (rules of thumb based on intuition / experience but with limited scientific basis). The purpose of this project is to replace these by machine learning techniques. Machine Learning (ML) is an overarching term for tools that allow computers to make decisions that are not explicitly programmed, usually involving the statistical analysis of large quantities of data. ML is quite at odds with the field of Symbolic Computation which studies QE, as the latter prizes exact correctness and so shuns the use of probabilistic tools making its application here very novel. We are able to combine these different worlds because the choices which we will use ML to make will all produce a correct and exact answer (but with different computational costs).

The project follows pilot studies undertaken by the PI which experimented with one ML technique and found it improved upon existing heuristics for two particular decisions in a QE algorithm. We will build on this by working with the spectrum of leading ML tools to identify the optimal techniques for application in Symbolic Computation. We will demonstrate their use for both low level algorithm decisions and choices between different theories and implementations. Although focused on QE, we will also demonstrate ML as being a new route to optimisation in Computer Algebra more broadly and work encompasses Project Partners and events to maximise this. Finally, the project will deliver an improved QE procedure that makes use of ML automatically, without user input. This will be produced in the commercial Computer Algebra software Maple in collaboration with industrial Project Partner Maplesoft.
Key Findings
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Potential use in non-academic contexts
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Further Information:  
Organisation Website: http://www.cov.ac.uk