Multi-UAV Systems

该项目将调查支持人机团队的机器学习算法的开发。这些团队包括人类通过广泛的协作和竞争关系与软件代理或机器人一起工作。在某些情况下，团队可能由人类负责，而在另一些情况下，则可能由代理人或机器人负责。正如我们之前的研究所表明的那样，这种灵活的自主形式要求机器具有主动性和学习能力，以便适应团队成员，特别是团队中人类成员的不同要求。设计这种算法所涉及的挑战包括：(I)处理稀疏数据集；(Ii)从有限的输入推断人类的偏好；(Iii)随着时间的推移和不同情况下的学习。该项目将着眼于对人机交互中使用的机器学习技术进行调查，重点是加强学习技术和学习系统的人机界面设计。随后，我们将开发设计人机合作算法和学习算法的方法。算法的评估将在两种可能的场景下进行：(I)辅助生活系统，其中人类依赖自治物联网平台来监控患有痴呆症的家庭成员；(Ii)多无人机系统，在非常动态的灾难应对环境中依赖最少的飞行员。该项目符合该大学对人工智能和自主系统的关注。特别是，主管将与ECS和工程部门的同事合作开发试验台并评估设计的算法。该项目也与新成立的由主管领导的机器智能中心完美一致。

The project will investigate the development of machine learning algorithms that will support human machine teams. These teams involve humans working alongside software agents or robots through a broad range of collaborative and competitive relationships. At some points humans may be in charge of the team and at others, agents or robots may be in charge. As previously shown in our studies, such forms of flexible autonomy require machines to be proactive and exhibit learning abilities in order to adapt their responses to the varying requirements of the individual team members and particularly the human members of the team. The challenges involved in designing such algorithms include (i) dealing with sparse datasets (ii) infering human preferences from limited inputs and (iii) learning over time and in different situations. The project will look at developing a survey of machine learning techniques used in human-machine interaction, with a focus on re-inforcement learning techniques and human-machine interface design for learning systems. Following this, we will develop methodologies to design human-machine teaming algorithms and learning algorithms.The evaluation of the algorithms will be done on real data with two possible scenarios: (i) assisted living systems where humans rely on autonomous IoT platforms to monitor family members with dementia and (ii) multi-UAV systems that rely on a minimum set of pilots in very dynamic disaster response settings.This project fits within the university's focus on AI and Autonomous Systems. In particular, the supervisor will be collaborating with colleagues across ECS and Engineering to develop testbeds and evaluate the algorithms designed. The project also perfectly aligns with the newly created Centre for Machine Intelligence directed by the supervisor.