Multi-agent Cooperative Decision Making using Genetic Cascading Fuzzy Systems
Nicholas D. Ernest, Eloy García, David W. Casbeer, Kelly De Oliveira Cohen, Corey J. Schumacher · 2015
Missions consisting of groups of unmanned aerial vehicles (UAVs) require a high degree of coordination for successfully achieving desired goals. From the many types of applications where a group of coordinated agents is potentially able to outperform a single or a number of systems operating independently, the assignment of tasks is an important one. Different authors have addressed multi-agent task assignment problems for UAVs. For instance, [4] presented a robust task assignment algorithm for uncertain environments. The authors of [1] provided a decentralized task consensus algorithm with asynchronous communication. Decisions to communicate are taken by each agent independently based on the outcomes of assignments using different sets of information. In the present paper we envision a cooperative assignment of tasks in which vehicles decide which threats to attack according to collective preferences, in contrast to individual preferences which has been a common approach in the literature [3, 7–10]. The main objective behind this approach is to encourage agents to make decisions that bring greater benefit to the group. Although this objective may not necessarily suit all assignment problems, in some scenarios like the one presented in this paper the collective approach can potentially lead to an improved cooperation among agents. Additionally, the assignments need to be free of conflicts in order to use resources wisely. In the present work, agents are equipped with different types of weapons which need to be used in optimal manner and according to the specific type of threats. All these different factors need to be considered in order to make good and fast decisions. The main tool used in this paper for each local agent to generate these decisions is based on a novel method of Genetic Cascading Fuzzy Systems [6].