A Model for Multi-Agent Autonomy That Uses Opinion Dynamics and Multi-Objective Behavior Optimization

This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretical results, we show it is possible to design the behavior of the MRS by the selection of a relatively small set of parameters. The resulting behavior - both collective actions and individual actions - can be understood intuitively. The approach is entirely decentralized and the communication cost scales by the number of group options, not agents. We demonstrated the effectiveness of this approach using a hypothetical ‘explore-exploit-migrate’ scenario in a two hour field demonstration with eight unmanned surface vessels (USVs). The results from our preliminary field experiment show the collective behavior is robust even with time-varying network topology and agent dropouts.

Authors

• Tyler M. Paine
• Michael R. Benjamin

Keywords

• Costs
• Network topology
• Multi-robot systems
• Optimization
• Multi-objective Optimization
• Opinion Dynamics
• Dynamical
• Field Experiments
• Individual Decisions
• Collective Behavior
• Number Of Options
• Multi-agent Systems
• Autonomous Surface Vehicles
• Model Parameters
• Network System
• Decision Variables
• Individual Agency
• Collective Learning
• Auction
• Algal Blooms
• Cooperative Behavior
• Detail In Paper
• Pareto Optimal
• Monte Carlo Tree Search
• Ith Agent
• Task Allocation
• Coalition Formation
• Competitive Behavior
• Adaptive Sampling
• Opinion Formation
• Individual Robots
• Changes In Network Topology