Title :
"Freecell" neural network heuristics
Author :
Dunphy, Alphonsus ; Heywood, Malcolm I.
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
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;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223768