NSF-BSF: RI: Small: Decentralized Active Goal Recognition

Northeastern University, Boston MA

Investigators

Abstract

Autonomous systems often need to coordinate with other sensors, robots, autonomous cars, and people. This results in multi-agent systems, in which agents must be able to determine what others are currently doing and predict what they will be doing in the future. This task of plan and goal recognition, typically relies upon a passive observer that continually observes the multi-agent system. In many real-world systems, such as assistive robotics in the home, this is not practical. Real-world systems will require active goal recognition, where information has a cost, and other tasks are pursued and completed continuously during goal recognition. For example, consider a team of robots assisting a disabled or an elderly person. The robots must fetch items and clean areas, while also opening doors or otherwise escorting the person. The agents will have to balance completion of their own tasks with information gathering about the target person's behavior. Current goal recognition methods cannot solve this active goal recognition problem. Furthermore, in realistic multi-agent domains including agricultural applications, disaster assistance, or military settings, communication will be limited or noisy. This will require decentralized active goal recognition methods where agents make choices based on their own limited viewpoints. Developing such active goal recognition methods will be the focus of this research. More specifically, the research will develop new methods for active goal recognition to allow teams of agents to coordinate with other systems. The project will develop methods for: active goal recognition, combining the observer's planning problem with goal recognition to balance information gathering with task completion for a single agent (observer) and single target, decentralized active goal recognition, combining multi-agent planning for the observers with goal recognition to balance information gathering with task completion and coordination for multiple observer agents and a single target agent, and decentralized active goal recognition of multiple targets, combining multi-agent planning for the observers with goal recognition to balance information gathering with task completion and coordination for multiple observer agents and target agents. The research will develop a range of methods that are based on classical, information-theoretic and decision- theoretic planning that exploit the special structure in our problem. The work will be tested on a range of common benchmarks, against current methods and in multi-robot domains to ensure realistic experiments. This research will consider active goal recognition (combining an observer's planning problem with goal recognition of a target) in single-agent and decentralized multi-agent environments. The resulting work will greatly extend the usefulness of goal recognition, making it realistic to use in scenarios when information gathering has a cost and other tasks may need to be completed by the observer(s). 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.