There are many different approaches to solving planning problems, one of which is the use of domain specific control knowledge to help guide a domain independent search algorithm. This paper presents L2Plan which represents this control knowledge as an ordered set of control rules, called a policy, and learns using genetic programming. The genetic program’s crossover and mutation operators are augmented by a simple local search. L2Plan was tested on both the blocks world and briefcase domains. In both domains, L2Plan was able to produce policies that solved all the test problems and which outperformed the hand-coded policies written by the authors.
|Title of host publication||Proceedings of the 22nd Workshop of the UK Planning and Scheduling Special Interest Group|
|Number of pages||13|
|Publication status||Published - 1 Dec 2003|
- genetic programming
- action strategies
- planning domains
Levine, J., & Humphreys, D. (2003). Learning action strategies for planning domains using genetic programming. In Proceedings of the 22nd Workshop of the UK Planning and Scheduling Special Interest Group