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

自主机器人系统决策中的一个基本问题是这些系统如何做出决策，使得这些决策与人类在类似情况下做出的决策相似。这方面的决策是很重要的，以便机器人代理生成可预测的行为，这是必要的，以建立信任的人机团队。解决这个问题的传统方法假设人类是消息灵通的，完全理性的代理人，总是达到最优（例如，最好的决定）。然而，一些实证研究表明，大多数人在做决策时并不是完全理性的，而是有限理性的。换句话说，他们并不试图无条件地获得最好的结果，而是倾向于在限制条件下（时间、认知努力、精力、金钱或仅仅是懒惰）衡量达到最佳结果的好处。使用这个关键的想法，本研究将探讨决策的自治系统资源约束。与人类如何做出决策类似，决策过程将不得不考虑智能体可用的计算、信息或能量约束，而不是无论如何都试图获得最佳决策。该项目研究了自主机器人代理与其他代理人或机器人之间的相互作用，同时考虑代理人的决策约束。而不是专注于最大化的预期效用（如在标准配方），这项研究将解决最大化的预期自由效用（或自由能），一个数量，封装在一个精确的方式的信息和计算约束的决策过程的影响。这项研究的结果将被应用到多个代理人可能有冲突的目标的情况下。 作为测试案例，该框架适用于交通中的自动驾驶车辆问题，包括人类驾驶员和其他自动驾驶车辆。通过适应代理的决策过程中的可用资源，这个框架可以用来生成一个层次的抽象决策所带来的严重性的信息处理成本。每个抽象都是根据问题的“正确”粒度级别定制的。也就是说，当计算资源稀缺时优选较粗的抽象级别，而当计算资源丰富时优选较细（更准确）的抽象级别。最后，“心理理论”原则将被用来设计有限理性的决策算法，避免冲突代理之间的无限回归。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无限回归。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.