RI: Small: Foundations and Applications of Observer-Aware Planning

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Artificial Intelligence (AI) is becoming ubiquitous in our daily lives. This frequent interaction between humans and AI, requires systems to be more cognizant of humans in the environment. This project develops a comprehensive approach for AI decision making in the presence of human observers. Without considering observers, AI systems could behave in a way that confuses, startles, or even threatens others. Humans often change their behavior in the presence of observers in a deliberate way, for example, to make their intention transparent and reassure an observer. Observer-aware behaviors may include explicit communication to convey intentions, for example, using hand gestures or light signals, as well as implicit communication through behavior. The project unifies a wide range of observer-aware behaviors designed to accomplish different goals, including legible behavior that implicitly conveys intentions via the choice of actions, explicable behavior that conforms to observers’ expectations, predictable behaviors that enable observers to predict future actions, as well as behaviors designed reveal the capabilities of the acting agent. These forms of observer-aware behaviors advance a key research priority in contemporary AI; that is, to produce human-centered systems that are easier to understand, predict, and collaborate. This project introduces a new model to study observer-aware planning called Observer-Aware Markov Decision Process (OAMDP). This model develops novel automated planning techniques that can optimize observer-aware objectives beyond the scope of existing planning techniques. Specific contributions include: (1) analyses of the computational complexity of observer-aware planning under different assumptions and the theoretical and practical differences between OAMDP and existing models; (2) development of both exact and approximate algorithms for efficient observer-aware planning; (3) implementation and evaluation of belief-update methods compatible with how humans perceive AI systems; and (4) extensions of the approach to manage the tradeoffs between observer-related objectives (e.g., improving predictability of intentions) and domain-related objectives (e.g., reducing the completion time of the assigned task) and incorporate explicit communication between the agent and the observer. The overarching goals of this project are to develop a general automated planning approach that can achieve a range of observer-related strategic objectives, analyze the theoretical complexity of these problems, develop efficient algorithms for observer-aware planning, and validate them via experiments with human subjects in realistic settings. The team conducts a comprehensive evaluation of the new planning paradigm and demonstrates experimentally the value of observer-aware planning in realistic settings involving observers interacting with mobile robots and autonomous vehicles, including use cases developed in collaborations with industry partners. 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.