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Action Selection methods using
A dissertation submitted for the degree of Doctor of Philosophy
in the University of Cambridge
This is the expanded version of my PhD,
Technical Report No. 426
in June 1997.
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The Action Selection problem
is the problem of run-time choice between conflicting and heterogenous goals,
a central problem in the simulation of whole creatures
(as opposed to the solution of isolated uninterrupted tasks).
This thesis argues that Reinforcement Learning has been overlooked in the solution of the Action Selection problem.
Considering a decentralised model of mind,
with internal tension and competition between selfish behaviors,
this thesis introduces an algorithm called "W-learning",
whereby different parts of the mind modify their behavior based on whether or not they are succeeding in
getting the body to execute their actions.
This thesis sets W-learning in context among the different ways of exploiting
Reinforcement Learning numbers for the purposes of Action Selection.
It is a `Minimize the Worst Unhappiness' strategy.
The different methods are tested and their strengths and weaknesses analysed
in an artificial world.
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