CAREER: Scalable Algorithms for Individual Decision Making in Multiagent Settings

该奖项的研究是开发在不确定环境中单个人工智能体与其他智能体共同居住的战略决策的高效和有效的方法。例如，自主无人驾驶飞行器应该如何决定是密切监视可能的逃犯，还是拦截可能意识到监视的目标？为了实现这一目标，研究正在确定计算复杂性的来源，并理解计算效率和决策有效性之间相互冲突的关系。在不确定的多智能体环境中，个体决策问题使用一个公认的框架进行形式化，该框架将部分可观察马尔可夫决策过程（pomdp）的决策理论范式与贝叶斯博弈和交互认识论的元素相结合。在这个被称为交互式POMDP （I-POMDP）的框架中，该研究利用了在多智能体设置中对上下文知识进行最小建模的创新方法，利用了新的决策启发式和问题中的嵌入式结构。研究和教育的整合体现在不确定条件下战略决策的多学科课程的开发和交付上，该课程将规范理论与真实的人类决策行为相结合并进行比较。通过将决策和博弈论的各个方面结合起来，这两个理论都试图理解规范的决策方式，并关注真实的人类决策行为，本研究有助于人工智能的长期研究和开发，这些人工智能可以在应急响应、环境可持续性、自动驾驶汽车等领域协助进行理性、长期的决策和规划。

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.