On the effects of team size and communication load on the performance in exploration games

Exploration games are games where agents (or robots) need to search resources and retrieve these resources. In principle, performance in such games can be improved either by adding more agents or by exchanging more messages. However, both measures are not free of cost and it is important to be able to assess the trade-off between these costs and the potential performance gain. The focus of this paper is on improving our understanding of the performance gain that can be achieved either by adding more agents or by increasing the communication load. Performance gain moreover is studied by taking several other important factors into account such as environment topology and size, resource-redundancy, and task size. Our results suggest that there does not exist a decision function that dominates all other decision functions, i.e. is optimal for all conditions. Instead we find that (i) for different team sizes and communication strategies different agent decision functions perform optimal, and that (ii) optimality of decision functions also depends on environment and task parameters. We also find that it pays off to optimize for environment topologies.