Prior studies have found that explaining robot decisions and actions helps to increase system transparency, improve user understanding, and enable effective human-robot collaboration. In this paper, we present a system for generating personalized explanations of robot path planning via user feedback. We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and corpus). In addition, we design the system to interact with users via answering users' further questions about the generated explanations. Users have the option to update their preferences to view different explanations. The system is capable of detecting and resolving any preference conflict via user interaction. The results of an online user study show that the generated personalized explanations improve user satisfaction, while the majority of users liked the system's capabilities of question-answering and conflict detection/resolution.
Providing explanations of chosen robotic actions can help to increase the transparency of robotic planning and improve users' trust. Social sciences suggest that the best explanations are contrastive, explaining not just why one action is taken, but why one action is taken instead of another. We formalize the notion of contrastive explanations for robotic planning policies based on Markov decision processes, drawing on insights from the social sciences. We present methods for the automated generation of contrastive explanations with three key factors: selectiveness, constrictiveness, and respo…
Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency
Department of Mechanical Engineering（机械工程系）；Department of Electrical Engineering（电气工程系）；National University of Singapore（新加坡国立大学）；Computer Science（计算机科学）；University of Michigan（密歇根大学）
There is a growing interest in designing autonomous agents that can work alongside humans. Such agents will undoubtedly be expected to explain their behavior and decisions. While generating explanations is an actively researched topic, most works tend to focus on methods that generate explanations that are one size fits all. As in the specifics of the user-model are completely ignored. The handful of works that look at tailoring their explanation to the user's background rely on having specific models of the users (either analytic models or learned labeling models). The goal of this work is th…
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…
Language-Guided Generation for Personalized Inspection Planning
Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente（代尔夫特理工大学电子工程、数学和计算机科学学院）；Department of Electrical Engineering, Wright State University（电气工程系，怀特州立大学）
University of Edinburgh（爱丁堡大学）；Five AI Ltd.（Five AI有限公司）
Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and queryable systems to increase trust levels. We propose a transparent, human-centric explanation generation method for autonomous vehicle motion planning and prediction based on an existing white-box system called IGP2. Our method integrates Bayesian networks with context-free generative rules and can give causal natural language explanations for the high-level driving behaviour of autonomous vehicles. Preliminary test…
Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory
Meituan Inc.（美团公司）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Beijing Institute of Technology（北京理工大学）；University of Edinburgh（爱丁堡大学）
End-users' trust in automated agents is important as automated decision-making and planning is increasingly used in many aspects of people's lives. In real-world applications of planning, multiple optimization objectives are often involved. Thus, planning agents' decisions can involve complex tradeoffs among competing objectives. It can be difficult for the end-users to understand why an agent decides on a particular planning solution on the basis of its objective values. As a result, the users may not know whether the agent is making the right decisions, and may lack trust in it. In this work…
School of Computing, Informatics and Decision Systems Engineering, Arizona State University（亚利桑那州立大学计算、信息与决策系统工程学院）
Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework. The right explanation provides the rationale behind an AI agent's decision-making. However, to maintain the human teammate's cognitive demand to comprehend the provided explanations, prior works have focused on providing explanations in a specific order or intertwining the explanation generation with plan execution. Moreover, these approaches do not consider the degree of details required to share throughout the provided explanations. In this work, we argue that the agent-generated ex…
Providing personalized explanations for recommendations can help users to understand the underlying insight of the recommendation results, which is helpful to the effectiveness, transparency, persuasiveness and trustworthiness of recommender systems. Current explainable recommendation models mostly generate textual explanations based on pre-defined sentence templates. However, the expressiveness power of template-based explanation sentences are limited to the pre-defined expressions, and manually defining the expressions require significant human efforts. Motivated by this problem, we propose …
Prior work on generating explanations in a planning and decision-making context has focused on providing the rationale behind an AI agent's decision making. While these methods provide the right explanations from the explainer's perspective, they fail to heed the cognitive requirement of understanding an explanation from the explainee's (the human's) perspective. In this work, we set out to address this issue by first considering the influence of information order in an explanation, or the progressiveness of explanations. Intuitively, progression builds later concepts on previous ones and is k…
Enabling humans to identify potential flaws in an agent's decision making is an important Explainable AI application. We consider identifying such flaws in a planning-based deep reinforcement learning (RL) agent for a complex real-time strategy game. In particular, the agent makes decisions via tree search using a learned model and evaluation function over interpretable states and actions. This gives the potential for humans to identify flaws at the level of reasoning steps in the tree, even if the entire reasoning process is too complex to understand. However, it is unclear whether humans wil…
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts. JEDAI features a novel synthesis of research ideas from integrated task and motion planning and explainable AI. JEDAI helps users create high-level, intuitive plans while ensuring that they will be executable by the robot. It also provides users customized explanations about errors and helps improve their understanding of AI planning as well as the limits and capabilities of the underlying robot system.
LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning
McCombs School of Business, University of Texas at Austin（德克萨斯大学奥斯汀分校麦克斯韦商学院）；Naveen Jindal School of Management, University of Texas at Dallas（德克萨斯大学达拉斯分校奈文·金达管理学院）；Mays Business School, Texas A&M University（德克萨斯农工大学梅斯商学院）；Wharton School, University of Pennsylvania（宾夕法尼亚大学沃顿商学院）
Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University（教育部道路与交通工程重点实验室，同济大学）；Cooperative Vehicle Infrastructure System (CVIS), Intelligent Transportation Products Department, AI Cloud Group, Baidu Inc.（协同车路系统（CVIS）、智能交通产品部、AI云团队，百度公司）；V2X AI Road Open Platform, AI Cloud Group, Baidu Inc.（V2X AI道路开放平台，AI云团队，百度公司）
Belief-Space Control for Personalized Cancer Treatment via Active Inference
American Association for Cancer Research（美国癌症研究协会）；AACR Project GENIE registry（AACR Project GENIE 注册中心）；AACR Project GENIE Biopharma Collaborative（AACR Project GENIE 生物制药合作组织）
University of Science and Technology of China（中国科学技术大学）
One of the challenges of task planning is to find out what causes the planning failure and how to handle the failure intelligently. This paper shows how to achieve this. The idea is inspired by the connected graph: each verticle represents a set of compatible \textit{states}, and each edge represents an \textit{action}. For any given initial states and goals, we construct virtual actions to ensure that we always get a plan via task planning. This paper shows how to introduce virtual action to extend action models to make the graph to be connected: i) explicitly defines static predicate (type, …
The Hamlyn Centre for Robotic Surgery（哈姆林机器人外科中心）；Institute of Global Health Innovation（全球健康创新研究所）；Imperial College London（帝国理工学院）
This paper presents a versatile robotic system for sewing 3D structured object. Leveraging on using a customized robotic sewing device and closed-loop visual servoing control, an all-in-one solution for sewing personalized stent graft is demonstrated. Stitch size planning and automatic knot tying are proposed as the two key functions of the system. By using effective stitch size planning, sub-millimetre sewing accuracy is achieved for stitch sizes ranging from 2mm to 5mm. In addition, a thread manipulator for thread management and tension control is also proposed to perform successive knot tyi…
Robots assisting humans in complex domains have to represent knowledge and reason at both the sensorimotor level and the social level. The architecture described in this paper couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason with qualitative and quantitative descriptions of knowledge and degrees of belief. Specifically, incomplete domain knowledge, including information that holds in all but a few exceptional situations, is represented as a Answer Set Prolog (ASP) program. The answer s…
University of Colorado Boulder（科罗拉多大学博尔德分校）；Technion（以色列理工学院）
Traditional multi-robot motion planning (MMP) focuses on computing trajectories for multiple robots acting in an environment, such that the robots do not collide when the trajectories are taken simultaneously. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. In this work, we propose a notion of explanation for a plan of MMP, based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint, clearly illustrating the safety of the plan. W…
