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Investigation of Different Seeding Strategies in a Genetic Planner

  • C. Henrik Westerberg
  • John Levine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

Planning is a difficult and fundamental problem of AI. An alternative solution to traditional planning techniques is to apply Genetic Programming. As a program is similar to a plan a Genetic Planner can be constructed that evolves plans to the plan solution. One of the stages of the Genetic Programming algorithm is the initial population seeding stage. We present five alternatives to simple random selection based on simple search. We found that some of these strategies did improve the initial population, and the efficiency of the Genetic Planner over simple random selection of actions.

Keywords

Genetic Program Initial Population World State Depth First Search Random Action 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • C. Henrik Westerberg
  • John Levine
    • 1
  1. 1.AI Applications InstituteUniversity of EdinburghEdinburghUK

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