Learning to Efficiently Plan in Flexible Distributed Organizations

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Teams of robots are expected to revolutionise industry and other other parts of society. However, decision making in such so-called multiagent systems (MASs) under uncertainty is computationally very complex. The decentralized partially observable Markov decision process (Dec-POMDP) framework facilitates principled formulation of such decision making problems, but currently there are no scalable solution methods that provide guarantees on task performance. To simplify coordination in MASs, agent organisations assign an abstracted, easier problem to each agent. Typically only the most rigid organisations, which completely decouple the agents, have led to clear computational benefits. However, these come at the expense of task performance: full decoupling means that agents can no longer collaborate to divide the workload. This project will focus on flexible distributed organisations (FDOs) for Dec-POMDPs, which restrict considered interactions to spatially nearby agents without imposing full decoupling. Currently no scalable decision making methods with guarantees on task performance exist for FDOs: the main goal of the project is to develop such methods along with the theory that supports their formalisation. To accomplish this goal, it will investigate the use of deep learning techniques to learn representations of 'influence' in FDOs and use those representations to develop novel planning methods. If successful, this will provide the proof-of-concept that learned influence representations can enable principled decision making in large-scale MASs. This will be the basis for a larger research program investigating such influence representations for different forms of abstraction and will spark applied research that investigates deployment of the developed algorithms in real robotic teams.