Social, Physical, and Computational Tradeoffs in Collaborative Multi-agent Territory Exploration Tasks

The performance of embodied multi-agent systems depends, in addition to the agent architectures of the employed agents, on their physical characteristics (e.g., sensory range, speed, etc.) and group properties (e.g., number of agents, types of agents, etc.). Consequently, it is difficult to evaluate the performance of a multi-agent system based on the performance of an agent architecture alone, even in homogeneous teams. In this paper, we propose a method for analyzing the performance of multi-agent systems based on the notion of "performance-cost-tradeoff," which attempts to determine the relations among different cost-dimensions by performing a performance sampling of these dimensions and comparing them relative to their associated costs. Specifically, we investigate the performance-cost tradeoffs of four candidate architectures for a multi-agent territory exploration task in which a group of agents is required to visit a set of checkpoints randomly placed in an environment in the shortest time possible. Performance tradeoffs between three dimensions (sensory range, group size, and prediction) are then used to illustrate the cost-benefit analyses performed to determine the best agent configurations for different practical settings.