abstract: A critical challenge in the design of AI systems that operate with humans in the loop is to be able to model the intentions and capabilities of the humans, as well as their beliefs and expectations of the AI system itself. This allows the AI system to be "human- aware" -- i.e. the human task model enables it to envisage desired roles of the human in joint action, while the human mental model allows it to anticipate how its own actions are perceived from the point of view of the human. In my research, I explore how these concepts of human-awareness manifest themselves in the scope of planning or sequential decision making with humans in the loop. To this end, I will show (1) how the AI agent can leverage the human task model to generate symbiotic behavior; and (2) how the introduction of the human mental model in the deliberative process of the AI agent allows it to generate explanations for a plan or resort to explicable plans when explanations are not desired. The latter is in addition to traditional notions of human-aware planning which typically use the human task model alone and thus enables a new suite of capabilities of a human-aware AI agent. Finally, I will explore how the AI agent can leverage emerging mixed-reality interfaces to realize effective channels of communication with the human in the loop. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2018

Promised some decades ago by researchers in artificial intelligence and robotics as an imminent breakthrough in our everyday lives, a robotic assistant that could work with us in our home and our workplace is a dream still far from being fulfilled. The work presented in this thesis aims at bringing this future vision a little closer to realization. Here, we start from the assumption that an efficient robotic helper should not impose constraints on users' activities, but rather perform its tasks unobtrusively to fulfill its goals and to facilitate people in achieving their objectives.  Also, the helper should be able to consider the outcome of possible future actions by the human users, to assess how those would affect the environment with respect to the agent's objectives, and to predict when its support will be needed. In this thesis we address two highly interconnected problems that are essential for the cohabitation of people and service robots: robot task planning and human activity recognition. First, we present human-aware planning, that is, our approach to robot high-level symbolic reasoning for plan generation. Human-aware planning can be applied in situations where there is a controllable agent, the robot, whose actions we can plan, and one or more uncontrollable agents, the human users, whose future actions we can only try to predict. In our approach, therefore, the knowledge of the users' current and future activities is an important prerequisite. We define human-aware as a new type of planning problem, we formalize the extensions needed by a classical planner to solve such a problem, and we present the implementation of a planner that satisfies all identified requirements. In this thesis we explore also a second issue, which is a prerequisite to the first one: human activity monitoring in intelligent environments. We adopt a knowledge driven approach to activity recognition, whereby a constraint-based domain description is used to correlate sensor readings to human activities. We validate our solutions to both human-aware planning and activity recognition both theoretically and experimentally, describing a number of explanatory examples and test runs in a real environment.

As more robots and sensors are deployed in work and home environments, there is a growing need for these devices to act with some degree of autonomy to fulfill their purpose. Automated planning can be used to synthesize plans of action that achieve this. The main challenge addressed in this thesis is to consider how the automated planning problem changes when considered in the context of environments that are populated by humans. Humans have their own plans, and automatically generated plans should not interfere with these. We refer to this as social acceptability. Opportunities for proactive behavior often arise during execution. The planner should be able to identify these opportunities and proactively plan accordingly. Both social acceptability and proactivity require the planner to identify relevant situations from available information. We refer to this capability as context-awareness, and it may require complex inferences based on observed human activities. Finally, planning may have to consider cooperation with humans to reach common goals or to enable robots and humans to support one another. This thesis analyzes the requirements that emerge from human-aware planning — what it takes to make automated planning socially acceptable, proactive, context aware, and to make it support cooperation with humans. We formally state the human-aware planning problem, and propose a planning and execution framework for human-aware planning that is based on constraint reasoning and flaw-resolution techniques, and which fulfills the identified requirements. This approach is modular and extendable: new types of constraints can be added and solvers can be exchanged and re-arranged. This allows us to address the identified requirements for humanaware planning. In particular, we introduce Interaction Constraints (ICs) for this purpose, and propose patterns of Ics for social acceptability, proactivity, and contextawareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system. introduce Interaction Constraints (ICs) for this purpose, and propose patterns of ICs for social acceptability, proactivity, and context-awareness. We also consider cooperative plans in which certain actions are assigned to humans and the implications that this has. We evaluate the proposed methods and patterns on a series of use cases, as well as a variety of domains including a real-world robotic system.