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

NSF-BSF：RI：小型：去中心化主动目标识别

基本信息

批准号：
1816382
负责人：
Christopher Amato
金额：
$ 47万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816382&HistoricalAwards=false
关键词：
NSF BSF RI Small Decentralized

项目摘要

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.

自主系统通常需要与其他传感器、机器人、自主汽车和人协调。这就产生了多智能体系统，其中智能体必须能够确定其他智能体当前在做什么，并预测它们将来会做什么。这个计划和目标识别的任务，通常依赖于一个被动的观察者，不断观察多智能体系统。在许多现实世界的系统中，例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的目标识别，其中信息是有成本的，并且在目标识别期间，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。机器人必须取物品和清洁区域，同时还要开门或以其他方式护送人。代理将不得不平衡完成自己的任务与收集有关目标人行为的信息。现有的目标识别方法无法解决这一主动目标识别问题。此外，在现实的多代理域，包括农业应用，灾难援助，或军事设置，通信将是有限的或嘈杂的。这将需要分散的主动目标识别方法，代理人根据自己有限的观点做出选择。开发这样的主动目标识别方法将是本研究的重点。更具体地说，该研究将开发主动目标识别的新方法，以允许代理团队与其他系统协调。该项目将制定方法，用于：主动目标识别，将观察者的规划问题与目标识别相结合，以平衡单个智能体的信息收集与任务完成（观察者）和单个目标，分散的主动目标识别，将观察者的多智能体规划与目标识别相结合，以平衡多个观察者智能体和单个目标智能体的信息收集与任务完成和协调，以及多目标的分散主动目标识别，将观察者的多智能体规划与目标识别相结合，以平衡多个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。这项研究将开发一系列的方法，是基于经典的，信息理论和决策理论的规划，利用我们的问题的特殊结构。这项工作将在一系列常见的基准上进行测试，针对当前的方法和多机器人领域，以确保真实的实验。本研究将考虑主动目标识别（结合观察员的规划问题与目标识别的目标）在单智能体和分散的多智能体环境。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员完成的情况下使用变得现实。该奖项反映了NSF的法定使命，并被认为值得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来支持。