THE MULTI-AGENT PLANNING PROBLEM

The purpose of this paper is to present a Multi-Agent planner for a team of autonomous agents. The approach is demonstrated by the Multi-Agent Planning Problem, which is a variant of the classical Multiple Traveling Salesmen Problem (MTSP): given a set of n goals/targets and a team of m agents, the optimal team strategy consists of finding m tours such that each target is visited only once and by only one agent, and the total cost of visiting all nodes is minimal. The proposed solution method is a Genetic Algorithm Inspired Steepest Descent (GAISD) method. To validate the approach, the method has been benchmarked against MTSPs and routing problems. Numerical experiments demonstrate the goodness of the approach.