Scalable Decentralised Decision Making and Optimisation in Heterogeneous Teams
George Mathews, Hugh F Durrant-Whyte, Mikhail Prokopenko · 2006
This paper considers the scenario where multiple autonomous agents must cooperate in making decisions to minimise a common team cost function. A distributed optimisation algorithm is presented. This allows each agent to incrementally refine their decisions while intermittently receiving updates from the team. A convergence analysis provides quantitative requirements on the frequency agents must communicate that is prescribed by the problem structure. The general problem requires every agent to have a model of every other agent in the system. To overcome this, a specific subset of systems, called partially separable, is defined. These systems only require each agent to have a combined summary of the rest of the system. This leads to the definition of an infinitely scalable system, which may contain an infinite number of agents while ensuring the local decisions will converge to the optimal team decision. Examples are given for reconnaissance or information gathering tasks