adjustable autonomy for uav supervision applications through mental workload assessment techniques

In recent years, unmanned aerial vehicles have received a significant attention in the research community, due to their adaptability in different applications, such as surveillance, disaster response, traffic monitoring, transportation of goods, first aid, etc. Nowadays, even though UAVs can be equipped with some autonomous capabilities, they often operate in high uncertainty environments in which supervisory systems including human in the control loop are still required. Systems envisaging decision-making capabilities and equipped with flexible levels of autonomy are needed to support UAVs controllers in monitoring operations. The aim of this paper is to build an adjustable autonomy system able to assist UAVs controllers by predicting mental workload changes when the number of UAVs to be monitored highly increases. The proposed system adjusts its level of autonomy by discriminating situations in which operators' abilities are sufficient to perform UAV supervision tasks from situations in which system suggestions or interventions may be required. Then, a user study was performed to create a mental-workload prediction model based on operators' cognitive demand in drone monitoring operations. The model is exploited to train the system developed to infer the appropriate level of autonomy accordingly. The study provided precious indications to be possibly exploited for guiding next developments of the adjustable autonomy system proposed

Addressing accountability in highly autonomous virtual assistants

Building from a survey specifically developed to address the rising concerns of highly autonomous virtual assistants; this paper presents a multi-level taxonomy of accountability levels specifically adapted to virtual assistants in the context of Human-Human-Interaction (HHI). Based on research findings, the authors recommend the integration of the variable of accountability as capital in the development of future applications around highly automated systems. This element inserts a sense of balance in terms of integrity between users and developers enhancing trust in the interactive process. Ongoing work is being dedicated to further understand to which extent different contexts affect accountability in virtual assistants

Automation in human-machine networks: how increasing machine agency affects human agency

© 2018, Springer International Publishing AG. Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, emergency management, and crowd evacuation are presented, shedding light on how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change

From apology to compensation: A multi-level taxonomy of trust reparation for highly automated virtual assistants

This paper presents a multi-level taxonomy of reparation levels specifically adapted to virtual assistants in the context of Human-Human-Interaction (HHI) with a specific focus on maintaining trust in the system. This taxonomy ranges from current models of apology to the newly integrated compensation area via a range of case studies specifically developed to address the rising concerns of unsupervised interactions in the context of Virtual Assistants (VA). Based on preliminary research, the author recommends the integration of reparation strategies as a fundamental variable in the ongoing development of VAs, as this element inserts a sense of balance in terms of vulnerability between users and developers to enhance trust in the interactive process. Present and future work is being dedicated to further understand how different contexts may affect integrity in highly automated virtual assistants

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

An assigned responsibility system for robotic teleoperation control

This paper proposes an architecture that explores a gap in the spectrum of existing strategies for robot control mode switching in adjustable autonomy. In situations where the environment is reasonably known and/or predictable, pre-planning these control changes could relieve robot operators of the additional task of deciding when and how to switch. Such a strategy provides a clear division of labour between the automation and the human operator(s) before the job even begins, allowing for individual responsibilities to be known ahead of time, limiting confusion and allowing rest breaks to be planned. Assigned Responsibility is a new form of adjustable autonomy-based teleoperation that allows the selective inclusion of automated control elements at key stages of a robot operation plan’s execution. Progression through these stages is controlled by automatic goal accomplishment tracking. An implementation is evaluated through engineering tests and a usability study, demonstrating the viability of this approach and offering insight into its potential applications

Estruturacao, construcao e exploracao de um modelo multicriterio de suporte a decisao

This thesis deals with the two great phases in Multicriteria Decision Aid: structuring and evaluation. Starting from the study of the established structuring approaches, a new integrated approach is developed, based on the concept of "point of view". Its aim is the construction of a "family of fundamental points of view", which in turn is the basis for the evaluation process. This process starts with the construction of local preference models ("criteria"). An original interactive technique ("MACBETH") is proposed for building cardinal criteria quantifying, for each fundamental point of view, value judgements about the actions. To complete the construction of a model of preferences, Multicriteria Aggregation Procedures are used. In particular, we study a family of procedures through "conclusive compensation", to deal with situations of poor inter-criteria preference information. In this framework, we define and study the properties of a "credibility index" of the overall preference for an action over another. Finally, we show how to use this model to make recommendations concerning different problematic contexts studied in chapter I. This thesis stands by simplicity and constructive interactions in decision aidingAvailable from Fundacao para a Ciencia e a Tecnologia, Servico de Informacao e Documentacao, Av. D. Carlos I, 126, 1200 Lisboa / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga

A user study of command strategies for mobile robot teleoperation

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (KRF-2007-D00042)