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

自主系统通常需要与其他传感器、机器人、自动驾驶汽车和人员进行协调。这就产生了多智能体系统，其中的智能体必须能够确定其他人当前正在做什么，并预测他们将来会做什么。这个任务的计划和目标识别，通常依赖于一个被动的观察者，不断地观察多智能体系统。在许多现实世界的系统中，比如家庭中的辅助机器人，这是不切实际的。现实世界的系统将需要主动目标识别，其中信息是有成本的，在目标识别期间，其他任务被不断地追求和完成。例如，考虑一组机器人帮助残疾人或老年人。机器人必须拿取物品和打扫区域，同时还要开门或以其他方式护送人。代理人必须在完成自己的任务和收集目标人的行为信息之间取得平衡。目前的目标识别方法无法解决这种主动目标识别问题。此外，在现实的多智能体领域，包括农业应用、灾害援助或军事设置，通信将受到限制或嘈杂。这将需要分散的主动目标识别方法，其中智能体根据自己有限的观点做出选择。开发这种主动目标识别方法将是本研究的重点。更具体地说，该研究将开发新的主动目标识别方法，以允许代理团队与其他系统协调。该项目将开发以下方法：主动目标识别，将观察者的规划问题与目标识别相结合，实现单智能体（观察者）和单目标的信息收集与任务完成的平衡；分散主动目标识别，将观察者的多智能体规划问题与目标识别相结合，实现多观察者智能体和单目标智能体的信息收集与任务完成和协调的平衡；将观察者的多智能体规划与目标识别相结合，平衡了多观察者智能体和目标智能体的信息收集与任务完成和协调。研究将开发一系列基于经典规划、信息论规划和决策论规划的方法，利用我们问题中的特殊结构。这项工作将在一系列共同的基准上进行测试，针对当前的方法和多机器人领域，以确保真实的实验。本研究将考虑单智能体和分散多智能体环境下的主动目标识别（将观察者的规划问题与目标的目标识别相结合）。由此产生的工作将大大扩展目标识别的有用性，使其在信息收集有成本和其他任务可能需要由观察者完成的情况下使用。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。

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.