SYNERGY: A Linear Planner Based on Genetic Programming

TL;DR

SYNERGY is a parallelizable linear planning system based on genetic programming that efficiently solves large conjunctive goal problems and extends to hierarchical and dynamic environments.

Contribution

It introduces a genetic programming-based linear planner with extensions for hierarchical planning and dynamic environments, capable of handling larger problems than existing planners.

Findings

Handles problem instances 10-100 times larger than UCPOP

Effective in domains like robot navigation and briefcase problems

Supports hierarchical and dynamic environment planning

Abstract

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.