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.
