Ant Colony Planning Algorithm Based on Planning Graph

Graphplan is an important algorithm of intelligent planning in recent years. It has promoted great development of intelligent planning. Firstly, the Graphplan algorithm will generate a planning graph by action level expanding and proposition level expanding alternatively. Secondly, a valid plan will be extracted from the planning graph by backtracking in exhaustive way. The plan extracting of the algorithm always consume too much time in this way. And the algorithm is apt to plunge into the local searching. In this paper, a way of plan solution searching by using the ant colony algorithm is given. That is the ACP (ant colony planner) algorithm. In the ant colony algorithm, positive feedback and distributed coordination are used to find the solution path. And the ant colony algorithm even has the characteristic of robustness, thus it has been successfully applied in many applications which are NP-hard problems. The searching of the ACP has the characteristic of global and parallel searching. And ACP has the ability of convergence acceleration in the solution searching. The experiments show that ACP is advantage ous especially in solving the large scale planning problems. To absorb the optimizing technique and the learning technique is a rising way in the study of the intelligence planning. Since the ant colony planning algorithm is just based on the optimizing technique, thus the ant colony planning algorithm is promising to make some better progresses in the study of intelligence planning area by using the ant colony planning method.