  In this paper we describe SYNERGY, which is a highly parallelizable, linear
planning system that is based on the genetic programming paradigm. Rather than
reasoning about the world it is planning for, SYNERGY uses artificial
selection, recombination and fitness measure to generate linear plans that
solve conjunctive goals. We ran SYNERGY on several domains (e.g., the briefcase
problem and a few variants of the robot navigation problem), and the
experimental results show that our planner is capable of handling problem
instances that are one to two orders of magnitude larger than the ones solved
by UCPOP. In order to facilitate the search reduction and to enhance the
expressive power of SYNERGY, we also propose two major extensions to our
planning system: a formalism for using hierarchical planning operators, and a
framework for planning in dynamic environments.
