Decision Making in Heterogenous Multiagent Systems

最近发布的“人工智能百年研究”强调了开发人类感知系统的重要性，这些系统在设计时明确考虑到人类是不可或缺的一部分。例如，人们可以设想与人类无缝交互的自治系统，根据需要利用计算代理和人类的优势，增强两者的能力。然而，为了实现这样的系统，有许多研究挑战需要解决，从界面的设计，以支持人机协作的有效交互的正式模型。一个中心的挑战是确保人类和代表他们的代理人的偏好和激励是适当的对齐，因为对激励措施的误解和错位可能导致意想不到的结果和不良行为。此外，系统必须明确设计，以确保稳健和透明的结果，尊重代理人（人和计算）的偏好和价值观。在这份提案中，我描述了一个研究议程，通过发展和勒韦林研究算法博弈论，计算社会选择，更广泛地说，多智能体系统和人工智能，将支持强大的人类感知系统的发展。 首先，它将解决多智能体系统和人工智能中的基本问题。在这方面的进展将支持强大的多代理系统，可以在广泛的应用领域的发展。最后，该提案的核心是培养高素质的人工智能人才，这是一个所有行业和部门都有强烈需求的国家领域。

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.