School of Computing. Dublin City University.
Online coding site: Ancient Brain
My PhD thesis is on the "Action Selection" problem - the problem of run-time choice between multiple parallel, competing, conflicting and overlapping goals, that must respond to unpredictable and passing opportunities in the world - in other words the "Operating System" for a multiple-goal autonomous creature. This is about the overall architecture of a mind - how does an animal, mobile robot or software agent decide which goals to pursue at a given moment, when to interrupt, and when to opportunistically divert, in response to events in the world? The thesis contains a Survey of Action Selection. For a shorter introduction see the SAB-96 paper.
This is a strongly decentralised model of mind, with internal tension and competition between selfish behaviors. I introduce an algorithm called "W-learning", whereby different parts of the mind modify their behaviour based on whether or not they are succeeding in getting the body to execute their actions. The thesis sets W-learning in context among the different ways of exploiting RL numbers for the purposes of Action Selection. It is a "Minimize the Worst Unhappiness" strategy.
My model of mind is radically decentralised, with the goals set up in free competition against each other. You can view it as Multiple Minds in the same Body. It is a numeric (non-symbolic) Society of Mind which functions as an individual-driven liberal democracy.
My model of mind can be seen as a primitive "Joycean machine" (see Dennett's Consciousness Explained).
It is ironic that I have come up with such a seething, competitive, multi-processor model of mind, since this is not the way I personally seem to work at all! Any of my friends will tell you that I am a mono-processor, who works on one thing at a time, devoting lots of concentration to it and being immune to interrupts. Of course my model can produce such a serial result - with all the frustrated competition still going on below the conscious level.
Return to my Research page.