Decision Making in Heterogenous Multiagent Systems

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The recently released ``One Hundred Year Study on Artificial Intelligence" emphasized the importance of developing human-aware systems, that are explicitly designed taking into consideration the fact that humans are an integral part. For example, one can envision autonomous systems that seamlessly interact with humans, leveraging the strengths of both computational agents and humans as required, augmenting the abilities of both. However, in order for such systems to be realized, there are numerous research challenges which need to be addressed, ranging from the design of interfaces to support human-machine collaboration to formal models of effective interaction. One central challenge is ensuring that preferences and incentives of both humans and the agents representing them are appropriately aligned, as misunderstandings and misalignment of incentives can lead to unexpected outcomes and undesirable behaviour. Furthermore, systems must be explicitly designed so as to ensure robust and transparent outcomes, respecting the agents' (both human and computational) preferences and values. In this proposal I describe a research agenda that, by developing and levering research in algorithmic game theory, computational social choice and, more broadly, multiagent systems and artificial intelligence, will support the development of robust human-aware systems. The impact of this research will be felt across multiple dimensions. First, it will address fundamental questions in multiagent systems and artificial intelligence. Progress in this area will support the development of robust multiagent systems that can used across a wide range of application domains. Finally, central to the proposal is the training of highly qualified personnel in artificial intelligence, an area where there is strong national demand across all industries and sectors.