CAREER: Scalable Algorithms for Individual Decision Making in Multiagent Settings

该奖项下的研究正在开发高效和有效的方法，通过在不确定环境中与其他代理共存的单个人工代理来制定战略决策。例如，自动无人驾驶飞行器应该如何决定是更密切地监视可能的逃犯，还是拦截可能知道监视的目标？为了实现这一目标，该研究旨在找出计算复杂性的来源，并了解计算效率和决策有效性之间相互冲突的关系。这个不确定多智能体环境下的个人决策问题是使用一个公认的框架来形式化的，该框架结合了部分可观测马尔可夫决策过程(POMDP)的决策理论范式与贝叶斯博弈和交互认识论的元素。在这个被称为交互式POMDP(I-POMDP)的框架中，该研究采用了在多智能体环境中最小限度地模拟上下文知识的创新方法，利用了新颖的决策启发式方法和问题的嵌入结构。研究和教育的结合体现在开发和提供了一门关于不确定条件下的战略决策的多学科课程，该课程将规范性理论和真实的人类决策行为进行了整合和比较。通过将决策和博弈论的两个方面结合起来，并关注真实的人类决策行为，本研究有助于长期研究和开发能够帮助理性、在应急响应、环境可持续性、自动驾驶汽车和许多其他领域的长期决策和规划。

Research under this award is developing efficient and effective methods for strategic decision making by an individual artificial agent cohabiting with other agents in uncertain environments. For example, how should an autonomous unmanned aerial vehicle decide between closer surveillance of a possible fugitive or intercepting the target who may be aware of the monitoring? Toward this goal, the research is identifying the sources of computational complexity and understanding the conflicting interrelationship between computational efficiency and decision-making effectiveness. This problem of individual decision making in uncertain multiagent settings is formalized using a recognized framework that combines the decision-theoretic paradigm of partially observable Markov decision processes (POMDPs) with elements of Bayesian games and interactive epistemology. In this framework, called interactive POMDP (I-POMDP), the research utilizes innovative ways of minimally modeling contextual knowledge in multiagent settings, exploits novel decision-making heuristics and embedded structure in problems.Integration of research and education is manifest in the development and delivery of a multi-disciplinary course on strategic decision making under uncertainty, which integrates and compares normative theories with real human decision-making behavior.By combining aspects of decision and game theories, both of which seek to understand normative ways of decision making, with attention to real human decision-making behavior, this research is contributing to long-term research and development of artificial agents that can assist with rational, long-term decision making and planning in areas including emergency response, environmental sustainability, autonomous vehicles and many others.