Swarming Polyagents Executing Hierarchical Task Networks

Swarming agents often operate in benign geographic topologies that let them explore alternative trajectories with minor variations that the agent dynamics then amplify for improved performance. In this paper we demonstrate the deployment of swarming agents in the non-metric and discontinuous topology of a process graph. We align our research with traditional AI approaches and focus on hierarchical task network (HTN) descriptions of constraints and preferences in the execution of abstract methods by a group of real-world entities. In particular, we adapt the TAEMS representation to place a greater emphasis on the mediation of method-execution through shared resources and collectively achieved quality (stigmergic coordination). The paper presents our polyagent model and experiments that demonstrate the scalability of the system and the ability of our agents to achieve optimal entity coordination.