S&AS: FND: Decision-Making for Autonomous Systems with Limited Resources

自主机器人系统决策的一个基本问题是，这些系统如何做出类似于人类在类似情况下做出的决策。为了让机器人代理产生可预测的行为，在人机团队中建立信任，这方面的决策制定是很重要的。解决这个问题的传统方法假设人类是消息灵通的、完全理性的主体，总是达到最优（例如，最佳决策）。然而，一些实证研究表明，大多数人在做决定时并不是完全理性的，而是有限度理性的。换句话说，他们不会无条件地去争取最好的结果，而是倾向于在一定的限制条件下（时间、认知努力、精力、金钱或只是懒惰）权衡达到最好结果的好处。利用这一关键思想，本研究将研究受资源约束的自治系统的决策。与人类如何做出决策类似，决策过程将不得不考虑代理可用的计算、信息或能量约束，而不是试图获得最优决策。本项目研究自主机器人代理与其他代理之间的交互，同时考虑代理的决策约束。而不是关注最大化预期效用（如在标准公式中），本研究将解决最大化预期自由效用（或自由能量），一个数量，以精确的方式封装的影响的信息和计算约束的决策过程。本研究的结果将应用于可能具有相互冲突目标的多个代理的情况。作为一个测试案例，该框架应用于自动驾驶车辆的交通问题，包括人类驾驶员和其他自动驾驶车辆。通过使智能体的决策过程适应可用资源，该框架可用于生成由信息处理成本严重程度带来的决策抽象层次。每个抽象都针对问题的“正确”粒度级别进行定制。也就是说，当计算资源稀缺时，首选较粗的抽象级别，而当计算资源丰富时，首选较细（更准确）的抽象级别。最后，利用“心智理论”原理设计有界理性决策算法，避免冲突主体之间的无限回归。这种无限回归阻碍了纯理性均衡的计算，通过采用限制每个参与者递归推理深度的框架可以避免这种情况。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A fundamental question in decision-making of autonomous robotic systems is how these systems make decisions such that these are similar to a decision a human would make in a similar situation. This aspect of decision making is important in order for the robotic agent to generate predictable behaviors which are necessary in order to establish trust in human-machine teams. Traditional approaches to solve this problem assume that humans are well-informed, perfectly rational agents that always reach the optimal (e.g., the best decision). Several empirical studies, however, suggest that most humans do not act as perfectly rational agents when making decisions but rather as bounded-rational ones. In other words, they do not try to get the best possible outcome unconditionally, but rather they tend to weight the benefits of reaching the best outcome subject to constraints (time, cognitive effort, energy, money, or just laziness). Using this key idea, this research will investigate decision-making for autonomous systems subject to resource constraints. Similar to how humans make decisions, instead of trying to get the optimal decision no matter what, the decision-making process will have to factor in the computational, information, or energy constraints available to the agent. This project investigates the interaction between autonomous robotic agents and other agents either human or robotic, while considering the decision-making constraints of the agents. Instead of focusing on maximizing expected utility (as in standard formulations) this research will address the maximization of expected free utility (or free energy), a quantity that encapsulates in a precise manner the effect of information and computational constraints of the decision-making process. The results of this research will be applied to the case of multiple agents having perhaps conflicting objectives. As a test case this framework is applied to the problem of autonomous vehicles in traffic including both human drivers and other self-driving vehicles. By adapting the agent's decision-making process to the available resources, this framework can be used to generate a hierarchy of abstractions for decision-making brought about by the severity of information-processing costs. Each abstraction is tailored to the "right" level of granularity of the problem. That is, coarser abstraction levels are preferred when the computational resources are scarce and finer (more accurate) levels of abstraction when the computational resources are plentiful. Finally, "theory of mind" principles will be utilized to design bounded-rational decision-making algorithms that avoid the infinite regress between conflicting agents. This infinite regress hinders the computation of purely rational equilibria and will be avoided by adopting a framework that restricts each player's depth of recursive inference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.