Anytime planning for agent behaviour

For an agent to act successfully in a complex and dynamic environment (such as a computer game)it must have a method of generating future behaviour that meets the demands of its environment. One such method is anytime planning. This paper discusses the problems and benefits associated with making a planning system work under the anytime paradigm, and introduces Anytime-UMCP (A-UMCP), an anytime version of the UMCP hierarchical task network (HTN) planner [Erol, 1995]. It also covers the necessary abilities an agent must have in order to execute plans produced by an anytime hierarchical task network planner

Sensing and indicating interruptibility in office workplaces

In office workplaces, interruptions by co-workers, emails or instant messages are common. Many of these interruptions are useful as they might help resolve questions quickly and increase the productivity of the team. However, knowledge workers interrupted at inopportune moments experience longer task resumption times, lower overall performance, more negative emotions, and make more errors than if they were to be interrupted at more appropriate moments. To reduce the cost of interruptions, several approaches have been suggested, ranging from simply closing office doors to automatically measuring and indicating a knowledge worker’s interruptibility - the availability for interruptions - to co-workers. When it comes to computer-based interruptions, such as emails and instant messages, several studies have shown that they can be deferred to automatically detected breakpoints during task execution, which reduces their interruption cost. For in-person interruptions, one of the most disruptive and time-consuming types of interruptions in office workplaces, the predominant approaches are still manual strategies to physically indicate interruptibility, such as wearing headphones or using manual busy lights. However, manual approaches are cumbersome to maintain and thus are not updated regularly, which reduces their usefulness. To automate the measurement and indication of interruptibility, researchers have looked at a variety of data that can be leveraged, ranging from contextual data, such as audio and video streams, keyboard and mouse interaction data, or task characteristics all the way to biometric data, such as heart rate data or eye traces. While studies have shown promise for the use of such sensors, they were predominantly conducted on small and controlled tasks over short periods of time and mostly limited to either contextual or biometric sensors. Little is known about their accuracy and applicability for long-term usage in the field, in particular in office workplaces. In this work, we developed an approach to automatically measure interruptibility in office workplaces, using computer interaction sensors, which is one type of contextual sensors, and biometric sensors. In particular, we conducted one lab and two field studies with a total of 33 software developers. Using the collected computer interaction and biometric data, we used machine learning to train interruptibility models. Overall, the results of our studies show that we can automatically predict interruptibility with high accuracy of 75.3%, improving on a baseline majority classifier by 26.6%. An automatic measure of interruptibility can consequently be used to indicate the status to others, allowing them to make a well-informed decision on when to interrupt. While there are some automatic approaches to indicate interruptibility on a computer in the form of contact list applications, they do not help to reduce in-person interruptions. Only very few researchers combined the benefits of an automatic measurement with a physical indicator, but their effect in office workplaces over longer periods of time is unknown. In our research, we developed the FlowLight, an automatic interruptibility indicator in the form of a traffic-light like LED placed on a knowledge worker's desk. We evaluated the FlowLight in a large-scale field study with 449 participants from 12 countries. The evaluation revealed that after the introduction of the FlowLight, the number of in-person interruptions decreased by 46% (based on 36 interruption logs), the awareness on the potential harm of interruptions was elevated and participants felt more productive (based on 183 survey responses and 23 interview transcripts), and 86% remained active users even after the two-month study period ended (based on 449 online usage logs). Overall, our research shows that we can successfully reduce in-person interruption cost in office workplaces by sensing and indicating interruptibility. In addition, our research can be extended and opens up new opportunities to further support interruption management, for example, by the integration of other more accurate biometric sensors to improve the interruptibility model, or the use of the model to reduce self-interruptions

A framework for intelligent mobile notifications

Mobile notifications provide a unique mechanism for real-time information delivery systems to users in order to increase its effectiveness. However, real-time notification delivery to users via mobile phones does not always translate into users' awareness about the delivered information because these alerts might arrive at inappropriate times and situations. Moreover, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges it is of paramount importance to devise intelligent notification mechanisms that monitor and learn users' behaviour for maximising their receptivity to the delivered information and adapt accordingly. The central goal of this dissertation is to build a framework for intelligent notifications that relies on the awareness of users' context and preferences. More specifically, we firstly investigate the impact of physical and cognitive contextual features on users' attentiveness and receptivity to notifications. Secondly, we construct and evaluate a series of models for predicting opportune moments to deliver notifications and mining users' notification delivery preferences in different situations. Finally, we design and evaluate a model for anticipating the right device notifications in cross-platform environments

Facilitating Reliable Autonomy with Human-Robot Interaction

Autonomous robots are increasingly deployed to complex environments in which we cannot predict all possible failure cases a priori. Robustness to failures can be provided by humans enacting the roles of: (1) developers who can iteratively incorporate robustness into the robot system, (2) collocated bystanders who can be approached for aid, and (3) remote teleoperators who can be contacted for guidance. However, assisting the robot in any of these roles can place demands on the time or effort of the human. This dissertation develops modules to reduce the frequency and duration of failure interventions in order to increase the reliability of autonomous robots, while also reducing the demand on humans. In pursuit of that goal, the dissertation makes the following contributions: (1) A development paradigm for autonomous robots that separates task specification from error recovery. The paradigm reduces burden on developers while making the robot robust to failures. (2) A model for gauging the interruptibility of collocated humans. A human-subjects study shows that using the model can reduce the time expended by the robot during failure recovery. (3) A human-subjects experiment on the effects of decision support provided to remote operators during failures. The results show that humans need both diagnosis and action recommendations as decision support during an intervention. (4) An evaluation of model features and unstructured Machine Learning (ML) techniques in pursuit of learning robust suggestions models from intervention data, in order to reduce developer effort. The results indicate that careful crafting of features can lead to improved performance, but that without such feature selection, current ML algorithms lack robustness in addressing a domain where the robot's observations are heavily influenced by the user's actions.Ph.D

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Inferring Cognitive Load using Wireless Signals

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging

Inferring Cognitive Load using Wireless Signals

From not disturbing a focused programmer, to entertaining a restless commuter waiting for a train, ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of the user's cognitive load. However, current means of assessing cognitive load are, with a few exceptions, based on intrusive methods requiring physical contact of the measurement equipment and the user. In this thesis we propose Wi-Mind, a system for remote cognitive load assessment through wireless sensing. Wi-Mind is based on a software-defined radio-based radar that measures sub-millimeter movements related to a person's breathing and heartbeats, which, in turn allow us to infer the person's cognitive load. We built the system and tested it with 23 volunteers being engaged in different tasks. Results show that while Wi-Mind manges to detect whether one is engaged in a cognitively demanding task, the inference of the exact cognitive load level remains challenging