• DocumentCode
    1923999
  • Title

    "Freecell" neural network heuristics

  • Author

    Dunphy, Alphonsus ; Heywood, Malcolm I.

  • Author_Institution
    Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
  • Volume
    3
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    2288
  • Abstract
    In areas, such as planning, state space searches are often conducted to find solutions. Usually, the heuristic is derived from knowledge of the domain. In many cases the knowledge of a domain is limited or the domain is so complex that an effective heuristic cannot be formulated. As an alternative, machine-learning techniques such as neural networks may be used to derive the heuristic. The game of Freecell was selected as a suitable benchmark domain, in which "knowledge based heuristics" and "neural heuristics" were employed to find solutions for randomly generated games. An amalgamation of the two, in which the neural network developed a heuristic from several knowledge based heuristics, was also used. Of the neural derived heuristics, the best-case architecture did not employ the "knowledge based heuristics". Moreover, neural heuristics were not able to improve upon those defined a priori.
  • Keywords
    computer games; learning (artificial intelligence); multilayer perceptrons; optimisation; search problems; self-organising feature maps; state-space methods; Freecell game; benchmark domain; knowledge based heuristics; machine learning techniques; multilayer perceptrons; neural network heuristics; organizing feature map; search heuristics; state space search; Computer science; Containers; Lifting equipment; Neural networks; Operating systems; State-space methods;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223768
  • Filename
    1223768