Human aware planning requires an agent to be aware of the intentions, capabilities and mental model of the human in the loop during its decision process. This can involve generating plans that are explicable to a human observer as well as the ability to provide explanations when such plans cannot be generated. This has led to the notion "multi-model planning" which aim to incorporate effects of human expectation in the deliberative process of a planner - either in the form of explicable task planning or explanations produced thereof. In this paper, we bring these two concepts together and show how a planner can account for both these needs and achieve a trade-off during the plan generation process itself by means of a model-space search method MEGA. This in effect provides a comprehensive perspective of what it means for a decision making agent to be "human-aware" by bringing together existing principles of planning under the umbrella of a single plan generation process. We situate our discussion specifically keeping in mind the recent work on explicable planning and explanation generation, and illustrate these concepts in modified versions of two well known planning domains, as well as a demonstration on a robot involved in a typical search and reconnaissance task with an external supervisor.
School of Computing and Augmented Intelligence, Arizona State University（计算与增强智能学院，亚利桑那州立大学）；School of Mathematical and Statistical Sciences, Arizona State University（数学与统计科学学院，亚利桑那州立大学）
The human-agent team, which is a problem in which humans and autonomous agents collaborate to achieve one task, is typical in human-AI collaboration. For effective collaboration, humans want to have an effective plan, but in realistic situations, they might have difficulty calculating the best plan due to cognitive limitations. In this case, guidance from an agent that has many computational resources may be useful. However, if an agent guides the human behavior explicitly, the human may feel that they have lost autonomy and are being controlled by the agent. We therefore investigated implicit…
Department of Computer Science, University of Maryland, College Park, MD, USA（计算机科学系，马里兰大学，学院公园，MD，美国）；Institute for Systems Research, University of Maryland, College Park, MD, USA（系统研究所，马里兰大学，学院公园，MD，美国）；Navy Center for Applied Research in Artificial Intelligence, U.S. Naval Research Laboratory, Washington, DC, USA（人工智能应用研究中心，美国海军研究实验室，华盛顿，DC，美国）
Stanford University（斯坦福大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
Adaptive Informative Path Planning with Multimodal Sensing (AIPPMS) considers the problem of an agent equipped with multiple sensors, each with different sensing accuracy and energy costs. The agent's goal is to explore the environment and gather information subject to its resource constraints in unknown, partially observable environments. Previous work has focused on the less general Adaptive Informative Path Planning (AIPP) problem, which considers only the effect of the agent's movement on received observations. The AIPPMS problem adds additional complexity by requiring that the agent reaso…
VORL-EXPLORE: A Hybrid Learning Planning Approach to Multi-Robot Exploration in Dynamic Environments
The University of Western Australia（西澳大学）；The Chinese University of Hong Kong（香港中文大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄大学）；Nanjing University（南京大学）
Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；University of Luxembourg（卢森堡大学）
Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability
University of Virginia（弗吉尼亚大学）；Google（谷歌）；University of Maryland, College Park（马里兰大学 College Park 分校）；Stanford University（斯坦福大学）；Louisiana State University（路易斯安那州立大学）；College of William and Mary（威廉与玛丽学院）
Existing approaches for generating human-aware agent behaviors have considered different measures of interpretability in isolation. Further, these measures have been studied under differing assumptions, thus precluding the possibility of designing a single framework that captures these measures under the same assumptions. In this paper, we present a unifying Bayesian framework that models a human observer's evolving beliefs about an agent and thereby define the problem of Generalized Human-Aware Planning. We will show that the definitions of interpretability measures like explicability, legibi…
Balancing Efficiency and Fairness: An Iterative Exchange Framework for Multi-UAV Cooperative Path Planning
The Hong Kong University of Science and Technology（香港科技大学）；College of Automation Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学自动化工程学院）；College of Computer Science and Engineering, Chongqing Three Gorges University（重庆三峡大学计算机科学与工程学院）；Chinese Academy of Sciences, New Territories, Hong Kong（中国科学院香港新 Territories 分院）；City University of Hong Kong, Kowloon, Hong Kong（香港城市大学）
