Philip G.K. Reiser
My principal research interests lie at the intersection of biology and and computer science, more specifically:
- Automation of Scientific Enquiry (Machine Learning)
- Automatic Theory Revision
- Automatic Selection of Experiments
- Computational Biology
- Functional Genomics
- Modelling Cellular Metabolism
- Inductive Logic Programming
- Logic Programming
- Evolutionary Models of Learning
- Evolutionary Computation
My PhD was concerned with the use of evolutionary algorithms to induce formulae
in first order logic.
Guest lecturer: Introduction to Artificial Intelligence (415.365) (University of Auckland)
Tutor: Software Reliability and Design (415.232) (University of Auckland)
Tutor: Programming courses in C, Ada, Unix (University of Wales)
Tutor: Discrete mathematics (University of Wales)
The Robot Scientist
Funded by BBSRC
Prof. Ross D. King (principal investigator, Aberystwyth)
Collaborators: Stephen Muggleton (Imperial), Steve Oliver (Manchester), Douglas Kell (UMIST)
We aim to provide a physical implementation of a scientific active learning
system, and apply it to the problem of functional genomics. The system will:
use machine learning to form an initial hypotheses set, devise experiments to
select between competing hypotheses, direct a robot to physically perform these
experiments, automatically analyse the experimental results, revise its
hypothesis set in the light of the experimental results, and then repeat the
cycle until the user's criteria for selection of the best hypothesis is met. In this way the system will automatically produce useful knowledge
about genes of unknown function.
Towards Automated Preprocessing in Data-Mining
Funded by The Boeing Company
Dr. Patricia Riddle (principal investigator, Auckland)
Michael Goebel (PhD student, Auckland)
Collaborators: Mike Barley (Auckland)
Most machine learning researchers estimate that 80-90% of the work in a
successful application of machine learning (or data-mining) is the preparation
work (combining data from different sources, selecting and creating new
attributes, choosing an appropriate representation for the data, and choosing
an appropriate machine learning algorithm). Yet most machine learning research
has focused on the 10-20% portion of the problem, running the machine learning
algorithm. We plan to address this situation by investigating the
data-engineering process. This will proceed with the development of
data-engineering tools that will reduce the labour involved in implementing
(14) King, R.D. Whelan, K.E., Jones, F.M., Reiser, P.G.K., Bryant, C.H.,
Muggleton, S.H., Kell, D.B. and Oliver, S.G. (2004) A robot scientist: automated hypothesis generation and experimentation for functional genomics.
Nature, 427, 247-252.
(13) Reiser, P.G.K., King, R.D. (2001) The Robot Scientist Platform. Technical report UWA-DCS-02-041. University of Wales.
(12) Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P.G.K., King, R.D. (2001)
Combining Inductive Logic Programming, Active Learning and
Robotics to Discover the Function of Genes.
Electronic Transactions in Artificial Intelligence 6(12).
(11) Reiser, P.G.K., King, R.D., Kell, D.B., Muggleton, S.H., Bryant, C.H., Oliver,S.G. (2001) Developing a Logical Model of Yeast Metabolism. Electronic Transactions in Artificial Intelligence 6(24).
(10) Reiser, P.G.K. and Riddle, P.J. (2001) Scaling up Inductive Logic Programming: an Evolutionary Wrapper Approach.
Applied Intelligence. 15(3), 181-197.
(9) Reiser, P.G.K. (1999) Evolutionary algorithms for learning formulae in first order logic. PhD Thesis. University of Wales, UK
(8) Reiser, P.G.K. and Freitas, A.A. (1999) A Survey of Evolutionary Algorithms for the Discovery of Classification Rules. (unpublished)
(7) Reiser, P.G.K. and Riddle, P.J. (1999) An Evolutionary Methodology for
Exploratory Machine Learning In: Proceedings of the Genetic and
Evolutionary Computation Conference. Florida. (submitted)
(6) Reiser, P.G.K. and Riddle, P.J. (1999) Evolution of Logic Programs:
Part-of-Speech Tagging In: Proceedings of the Congress on Evolutionary
Computation. Washington D.C. Vol 2. pp. 1338-1345.
(5) Reiser, P.G.K. and Riddle, P.J. (1998) Evolving Logic Programs to
Classify Chess-Endgame Positions In: Simulated Evolution and
Learning. Canberra, Australia. Lecture Notes in Artificial Intelligence
No. 1585. B. McKay et al (eds). Springer-Verlag. pp. 138-145.
(4) Reiser, P.G.K. (1998) Evolutionary Computation and the Tinkerer's
In: Proceedings of the First European Workshop on Genetic Programming. Paris, France. Lecture Notes in Computer Science. Wolfgang Banzhaf, Riccardo Poli, Marc Schoenauer and Terence C. Fogarty (eds). Springer-Verlag pp. 209-219.
(3) Reiser, P.G.K. (1998) Computational Models of Evolutionary Learning
In: Apprentissage: des principes naturels aux methodes artificielles. Ritschard, Berchtold, Duc and Zighed (editors). Hermes, Paris. pp. 291-306.
(2) Reiser, P.G.K. (1997) An Evolutionary Learning System for Knowledge Discovery (extended abstract)
In: Abstracts of the International Association of Interdisciplinary Research Conference on Learning. Geneva, Switzerland pp.234-237.
(1) Reiser, P.G.K. (1997) EVIL1: a Learning System to Evolve Logical Theories
In: Proceedings of the International Workshop on Logic Programming and Multi-Agents. Leuven, Belgium pp.95-101.
Prof. Ted Baker, University of Auckland.
Dr. Guillem Bernat, University of York.
Prof. John Fraser, University of Auckland.
Dr. Alex Freitas, University of Kent.
Prof. Doug Kell, UMIST.
Prof. Ross D. King, University of Wales.
Prof. Stephen Muggleton, Imperial College, London.
Prof. Steve Oliver, University of Manchester.
Dr. Pat Riddle, University of Auckland, NZ.