Knowledge Representation Issues in Control Knowledge Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{oai:CiteSeerPSU:341634,
  title =        "Knowledge Representation Issues in Control Knowledge
                 Learning",
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  booktitle =    "Proceedings of the Seventeenth International
                 Conference on Machine Learning (ICML 2000)",
  year =         "2000",
  editor =       "Pat Langley",
  pages =        "1--8",
  address =      "Stanford University, Standord, CA, USA",
  month =        jun # " 29 - " # jul # " 2",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, EBL, HAMLET,
                 EVOCK",
  ISBN =         "1-55860-707-2",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  URL =          "http://scalab.uc3m.es/~dborrajo/papers/icml00.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/341634.html",
  citeseer-isreferencedby = "oai:CiteSeerPSU:42967",
  citeseer-references = "oai:CiteSeerPSU:104987; oai:CiteSeerPSU:15322;
                 oai:CiteSeerPSU:554819",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:341634",
  rights =       "unrestricted",
  size =         "8 pages",
  abstract =     "Knowledge representation is a key issue for any
                 machine learning task. There have already been many
                 comparative studies about knowledge representation with
                 respect to machine learning in classification tasks.
                 However, apart from some work done on reinforcement
                 learning techniques in relation to state
                 representation, very few studies have concentrated on
                 the effect of knowledge representation for machine
                 learning applied to problem solving, and more
                 specifically, to planning. In this paper, we present an
                 experimental comparative study of the effect of
                 changing the input representation of planning domain
                 knowledge on control knowledge learning. We show
                 results in two classical domains using three different
                 machine learning systems, that have previously shown
                 their effectiveness on learning planning control
                 knowledge: a pure EBL mechanism, a combination of EBL
                 and induction (HAMLET), and a Genetic Programming based
                 system (EVOCK).",
  notes =        "http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2000.html",
}

Genetic Programming entries for Ricardo Aler Mur Daniel Borrajo Pedro Isasi Vinuela

Citations