Effects of automation on situation awareness in controlling robot teams

Declines in situation awareness (SA) often accompany automation. Some of these effects have been characterized as out-of-the-loop, complacency, and automation bias. Increasing autonomy in multi-robot control might be expected to produce similar declines in operators’ SA. In this paper we review a series of experiments in which automation is introduced in controlling robot teams. Automating path planning at a foraging task improved both target detection and localization which is closely tied to SA. Timing data, however, suggested small declines in SA for robot location and pose. Automation of image acquisition, by contrast, led to poorer localization. Findings are discussed and alternative explanations involving shifts in strategy proposed

CABINS: Case-based interactive scheduler

In this paper we discuss the need for interactive factory schedule repair and improvement, and we identify case-based reasoning (CBR) as an appropriate methodology. Case-based reasoning is the problem solving paradigm that relies on a memory for past problem solving experiences (cases) to guide current problem solving. Cases similar to the current case are retrieved from the case memory, and similarities and differences of the current case to past cases are identified. Then a best case is selected, and its repair plan is adapted to fit the current problem description. If a repair solution fails, an explanation for the failure is stored along with the case in memory, so that the user can avoid repeating similar failures in the future. So far we have identified a number of repair strategies and tactics for factory scheduling and have implemented a part of our approach in a prototype system, called CABINS. As a future work, we are going to scale up CABINS to evaluate its usefulness in a real manufacturing environment

An empirical study of argumentation schemes in deliberative dialogue

Publisher PD

Human control strategies for multi-robot teams

Expanding human span of control over teams of robots presents an obstacle to the wider deployment of robots for practical tasks in a variety of areas. One difficulty is that many different types of human interactions may be necessary to maintain and control a robot team. We have developed a taxonomy of human-robot tasks based on complexity of control that helps explicate the forms of control likely to be needed and the demands they pose to human operators. In this paper we use research from two of these areas to illustrate our taxonomy and its utility in characterizing and improving human-robot interaction

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors

Situating Cognition within the Virtual World

Cognitive architectures and virtual environments have a long history of use within the cognitive science community. Few studies, however, have sought to combine the use of these technologies to support computational studies into embodied, extended, situated and distributed (EESD) cognition. Here, we explore the extent to which the ACT-R cognitive architecture and the Unity game engine can be used for these purposes. A range of issues are discussed including the respective responsibilities that the cognitive architecture and game engine have for the implementation of specific processes, the extent to which the representational and computational capabilities of cognitive architectures are suited to the modeling of EESD cognitive systems, and the extent to which the kind of embodiment seen in the case of so-called ‘embodied virtual agents’ resembles that seen in the case of real-world bio-cognitive systems. These issues are likely to inform the focus of future research efforts concerning the integrative use of virtual environments and cognitive architectures for the computational modeling and simulation of EESD cognitive processes

Situating Cognition within the Virtual World

Cognitive architectures and virtual environments have a long history of use within the cognitive science community. Few studies, however, have sought to combine the use of these technologies to support computational studies into embodied, extended, situated and distributed (EESD) cognition. Here, we explore the extent to which the ACT-R cognitive architecture and the Unity game engine can be used for these purposes. A range of issues are discussed including the respective responsibilities that the cognitive architecture and game engine have for the implementation of specific processes, the extent to which the representational and computational capabilities of cognitive architectures are suited to the modeling of EESD cognitive systems, and the extent to which the kind of embodiment seen in the case of so-called ‘embodied virtual agents’ resembles that seen in the case of real-world bio-cognitive systems. These issues are likely to inform the focus of future research efforts concerning the integrative use of virtual environments and cognitive architectures for the computational modeling and simulation of EESD cognitive processes