PhD in Artificial Intelligence from the Department of Computing, Goldsmiths College, University of London.
I have a profile on Google Scholar.
Please feel free to email me at andrew@aomartin.co.uk.
People have said nice things about the second chapter of my thesis "The History of AI", so I've made it available as a stand alone chapter.
I have written a short opinion piece which aims to reduce the focus on algorithms in Artificial Intelligence, arguing that the implementation of a machine learning algorithm is merely step 3 in the four steps of an artificial intelligence project.
My thesis "Local Halting Criteria for Stochastic Diffusion Search Using Nature-inspired Quorum Sensing" is available here. The abstract is below:
Stochastic Diffusion Search (SDS) is a Swarm Intelligence algorithm in which a population of homogeneous agents locate a globally optimal solution in a search space through repeated iteration of partial evaluation and communication of hypotheses.
In this work a variant of SDS, Quorum Sensing SDS (QSSDS), is developed in which agents employ only local knowledge to determine whether the swarm has successfully converged on a solution of sufficient quality, and should therefore halt.
It is demonstrated that this criterion performs at least as well as SDS in locating the optimal solution in the search space, and that the parameters of Quorum Sensing SDS may be tuned to optimise behaviour towards a fast decision or a high quality solution.
Additionally it is shown that Quorum Sensing SDS can be used as a model of distributed decision making and hence makes testable predictions about the capacities and abilities of swarms in nature.
My book, co-edited with J. Mark Bishop, Contemporary Sensorimotor Theory was published by Springer in their SAPERE series (BibTex) in April 2014.
I also co-authored the first chapter, Contemporary Sensorimotor Theory: A Brief Introduction. (BibTex)