Collaborative Research: Distributed Interpretation in a Communication-Limited Environment

该项目将扩展先前在设计令人满意的多智能体系统的决策理论方法上的工作，以便将该方法应用于大规模，真实世界的传感器网络。该方法基于分布式贝叶斯网络和分散的马尔可夫决策过程。就智力价值而言，本研究解决了对支持多智能体系统设计的形式化技术的关键需求。近似的、令人满意的方法可以在信息质量和资源使用之间做出适当的权衡，从而减少构建大规模传感器网络以及其他大规模多智能体系统的障碍。就更广泛的影响而言，该项目最终可能通过鼓励发展复杂的大规模传感器网络而产生实质性的积极社会影响。这种大规模传感器网络有许多军事和民用应用。它们可以用来增强安保和安全，也可以使计算机化系统更加自主和灵活。

This project will extend previous work on a decision theoretic approach for designing satisficing multiagent systems so that it is practical to apply the approach to large-scale, real-world sensor networks. The approach is based on distributed Bayesian networks and decentralized Markov decision processes. In terms of intellectual merit, this research addresses a critical need for formal techniques to support the design of multiagent systems. Approximate, satisficing approaches can reason about the appropriate tradeoffs between information quality and resource usage and, thus, reduce barriers to building large-scale sensor networks, as well as other large-scale multiagent systems. In terms of broader impact, the project could ultimately have a substantial positive societal impact by encouraging the development of sophisticated, large-scale sensor networks. Such large-scale sensor networks have numerous military and civilian applications. They can be used to enhance security and safety, as well as to make computerized systems more autonomous and flexible.