Resource-aware plan recognition in instrumented environments

This thesis addresses the problem of plan recognition in instrumented environments, which is to infer an agent';s plans by observing its behavior. In instrumented environments such observations are made by physical sensors. This introduces specific challenges, of which the following two are considered in this thesis: - Physical sensors often observe state information instead of actions. As classical plan recognition approaches usually can only deal with action observations, this requires a cumbersome and error-prone inference of executed actions from observed states. - Due to limited physical resources of the environment it is often not possible to run all sensors at the same time, thus sensor selection techniques have to be applied. Current plan recognition approaches are not able to support the environment in selecting relevant subsets of sensors. This thesis proposes a two-stage approach to solve the problems described above. Firstly, a DBN-based plan recognition approach is presented which allows for the explicit representation and consideration of state knowledge. Secondly, a POMDP-based utility model for observation sources is presented which can be used with generic utility-based sensor selection algorithms. Further contributions include the presentation of a software toolkit that realizes plan recognition and sensor selection in instrumented environments, and an empirical evaluation of the validity and performance of the proposed models.Diese Arbeit behandelt das Problem der Planerkennung in instrumentierten Umgebungen. Ziel ist dabei das Erschließen der Pläne des Nutzers anhand der Beobachtung seiner Handlungen. In instrumentierten Umgebungen erfolgt diese Beobachtung über physische Sensoren. Dies wirft spezifische Probleme auf, von denen zwei in dieser Arbeit näher betrachtet werden: - Physische Sensoren beobachten in der Regel Zustände anstelle direkter Nutzeraktionen. Klassische Planerkennungsverfahren basieren jedoch auf der Beobachtung von Aktionen, was bisher eine aufwendige und fehlerträchtige Ableitung von Aktionen aus Zustandsbeobachtungen notwendig macht. - Aufgrund beschränkter Resourcen der Umgebung ist es oft nicht möglich alle Sensoren gleichzeitig zu aktivieren. Aktuelle Planerkennungsverfahren bieten keine Möglichkeit, die Umgebung bei der Auswahl einer relevanten Teilmenge von Sensoren zu unterstützen. Diese Arbeit beschreibt einen zweistufigen Ansatz zur Lösung der genannten Probleme. Zunächst wird ein DBN-basiertes Planerkennungsverfahren vorgestellt, das Zustandswissen explizit repräsentiert und in Schlussfolgerungen berücksichtigt. Dieses Verfahren bildet die Basis für ein POMDP-basiertes Nutzenmodell für Beobachtungsquellen, das für den Zweck der Sensorauswahl genutzt werden kann. Des Weiteren wird ein Toolkit zur Realisierung von Planerkennungs- und Sensorauswahlfunktionen vorgestellt sowie die Gültigkeit und Performanz der vorgestellten Modelle in einer empirischen Studie evaluiert

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space

We are developing a system for human-robot communication that enables people to communicate with robots in a natural way and is focused on solving problems in a shared space. Our strategy for developing this system is fundamentally data-driven: we use data from multiple input sources and train key components with various machine learning techniques. We developed a web application that is collecting data on how two humans communicate to accomplish a task, as well as a mobile laboratory that is instrumented to collect data on how two humans communicate to accomplish a task in a physically shared space. The data from these systems will be used to train and fine-tune the second stage of our system, in which the robot will be simulated through software. A physical robot will be used in the final stage of our project. We describe these instruments, a test-suite and performance metrics designed to evaluate and automate the data gathering process as well as evaluate an initial data set.Comment: 2017 AAAI Fall Symposium on Natural Communication for Human-Robot Collaboratio

Mid-Air Haptics for Control Interfaces

Control interfaces and interactions based on touch-less gesture tracking devices have become a prevalent research topic in both industry and academia. Touch-less devices offer a unique interaction immediateness that makes them ideal for applications where direct contact with a physical controller is not desirable. On the other hand, these controllers inherently lack active or passive haptic feedback to inform users about the results of their interaction. Mid-air haptic interfaces, such as those using focused ultrasound waves, can close the feedback loop and provide new tools for the design of touch-less, un-instrumented control interactions. The goal of this workshop is to bring together the growing mid-air haptic research community to identify and discuss future challenges in control interfaces and their application in AR/VR, automotive, music, robotics and teleoperation

On the effect of SNR and superdirective beamforming in speaker diarisation in meetings

This paper examines the effect of sensor performance on speaker diarisation in meetings and investigates the use of more advanced beamforming techniques, beyond the typically employed delay-sum beamformer, for mitigating the effects of poorer sensor performance. We present superdirective beamforming and investigate how different time difference of arrival (TDOA) smoothing and beamforming techniques influence the performance of state-of-the-art diarisation systems. We produced and transcribed a new corpus of meetings recorded in the instrumented meeting room using a high SNR analogue and a newly developed low SNR digital MEMS microphone array (DMMA.2). This research demonstrates that TDOA smoothing has a significant effect on the diarisation error rate and that simple noise reduction and beamforming schemes suffice to overcome audio signal degradation due to the lower SNR of modern MEMS microphones. Index Terms — Speaker diarisation in meetings, digital MEMS microphone array, time difference of arrival (TDOA), superdirective beamforming 1

Current research on aviation weather (bibliography), 1979

The titles, managers, supporting organizations, performing organizations, investigators and objectives of 127 current research projects in advanced meteorological instruments, forecasting, icing, lightning, visibility, low level wind shear, storm hazards/severe storms, and turbulence are tabulated and cross-referenced. A list of pertinent reference material produced through the above tabulated research activities is given. The acquired information is assembled in bibliography form to provide a readily available source of information in the area of aviation meteorology