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Learning the Impact of Group Structure on Optimal Herd Path Planning with Cultural Algorithms

This paper compares several approaches to cooperative multi-agent path planning (MAP) based upon variations of the A* algorithm. To simulate multi-agent migration patterns three path-finding mechanisms based on the classic A* algorithm was utilized: A*; A*, Ambush and Dendriform. Each makes different assumptions about group leadership in terms of their path generation. A* assumes a single leader for the migratory group: A*mbush allows the group to move in waves; and Dendriform allows the group to decompose and recompose into groups of arbitrary size with local leaders. Each mechanism required parameter weightings so that the simulated agents would interact realistically with their environment. Cultural Algorithms were employed to adjust the parameter weight categories in order to optimize the group movement under each of these leadership strategies. The three approaches were applied to the simulation of a real-world multi-agent system, the migration of large herd of caribou. The simulated migration was part of the Deepdive Virtual Reality system. In those simulations A* with a single planning agent emphasized nutrition at the expense of the other parameters. A*mbush learned to reduce nutrition slightly and while increasing its emphasis on risk and exploration. On the other hand, Dendriform emphasized overall effort since its planning more dynamic and required more concentration on local effort to be optimized.