- author = "Andrew David Bennett",
- title = "Using genetic programming to learn predictive models from spatio-temporal data",
- school = "School of Computing, University of Leeds",
- year = "2010",
- address = "UK",
- month = jul,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://etheses.whiterose.ac.uk/1376/",
-
URL = "
http://etheses.whiterose.ac.uk/1376/1/bennett_a.pdf",
-
URL = "http://ethos.bl.uk/OrderDetails.do?did=43&uin=uk.bl.ethos.530613",
- size = "211 pages",
-
abstract = "This thesis describes a novel technique for learning
predictive models from nondeterministic spatio-temporal
data. The prediction models are represented as a
production system, which requires two parts: a set of
production rules, and a conflict resolver. The
production rules model different, typically
independent, aspects of the spatio-temporal data. The
conflict resolver is used to decide which sub-set of
enabled production rules should be fired to produce a
prediction. The conflict resolver in this thesis can
probabilistically decide which set of production rules
to fire, and allows the system to predict in
non-deterministic situations. The predictive models are
learnt by a novel technique called Spatio-Temporal
Genetic Programming (STGP). STGP has been compared
against the following methods: an Inductive Logic
Programming system (Progol), Stochastic Logic Programs,
Neural Networks, Bayesian Networks and C4.5, on
learning the rules of card games, and predicting a
person's course through a network of CCTV cameras.
This thesis also describes the incorporation of qualitative temporal relations within these methods. Allen's intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time are used. The methods are evaluated on the card game Uno, and predicting a person's course through a network of CCTV cameras. This work is then extended to allow the methods to use qualitative spatial relations. The methods are evaluated on predicting a person's course through a network of CCTV cameras, aircraft turnarounds, and the game of Tic Tac Toe.
Finally, an adaptive bloat control method is shown. This looks at adapting the amount of bloat control used during a run of STGP, based on the ratio of the fitness of the current best predictive model to the initial fitness of the best predictive model.",
- notes = "noughts and crosses. Not strongly typed GP. uk.bl.ethos.530613",
