Using genetic programming to learn predictive models from spatio-temporal data

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{bennett_a,
  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",
}

Genetic Programming entries for Andrew Bennett

Citations