Learning to Solve Planning Problems Efficiently by Means of Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Learning to Solve Planning Problems Efficiently by
                 Means of Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2001",
  volume =       "9",
  number =       "4",
  pages =        "387--420",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, genetic
                 planning, evolving heuristics, planning, search. EvoCK,
                 STGP, blocks world, logistics, Prodigy4.0, STRIPS,
  ISSN =         "1063-6560",
  URL =          "http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841",
  DOI =          "doi:10.1162/10636560152642841",
  size =         "34 pages",
  abstract =     "Declarative problem solving, such as planning, poses
                 interesting challenges for Genetic Programming (GP).
                 There have been recent attempts to apply GP to planning
                 that fit two approaches: (a) using GP to search in plan
                 space or (b) to evolve a planner. In this article, we
                 propose to evolve only the heuristics to make a
                 particular planner more efficient. This approach is
                 more feasible than (b) because it does not have to
                 build a planner from scratch but can take advantage of
                 already existing planning systems. It is also more
                 efficient than (a) because once the heuristics have
                 been evolved, they can be used to solve a whole class
                 of different planning problems in a planning domain,
                 instead of running GP for every new planning problem.
                 Empirical results show that our approach (EVOCK) is
                 able to evolve heuristics in two planning domains (the
                 blocks world and the logistics domain) that improve
                 PRODIGY4.0 performance. Additionally, we experiment
                 with a new genetic operator Instance-Based Crossover
                 that is able to use traces of the base planner as raw
                 genetic material to be injected into the evolving

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