We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.

/pdf/a-model-for-types-and-levels-of-human-interaction-with-1wqlqxv26i.pdf

A model for types and levels of human interaction with automation

Explanation in artificial intelligence: Insights from the social sciences

This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to over reliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by man...

Humans and automation: Use, misuse, disuse, abuse

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

The Visual Display of Quantitative Information

In the future, it will become more common for humans to team up with robotic systems to perform tasks that humans cannot realistically accomplish alone. Even for autonomous and semiautonomous systems, teleoperation will be an important default mode. However, teleoperation can be a challenging task because the operator is remotely located. As a result, the operator's situation awareness of the remote environment can be compromised and the mission effectiveness can suffer. This paper presents a detailed examination of more than 150 papers covering human performance issues and suggested mitigation solutions. The paper summarizes the performance decrements caused by video images bandwidth, time lags, frame rates, lack of proprioception, frame of reference, two-dimensional views, attention switches, and motion effects. Suggested solutions and their limitations include stereoscopic displays, synthetic overlay, multimodal interfaces, and various predicative and decision support systems.

Human Performance Issues and User Interface Design for Teleoperated Robots

The human factors literature on intelligent systems was reviewed in relation to the following: efficient human supervision of multiple robots, appropriate human trust in the automated systems, maintenance of human operator's situation awareness, individual differences in human-agent (H-A) interaction, and retention of human decision authority. A number of approaches-from flexible automation to autonomous agents-were reviewed, and their advantages and disadvantages were discussed. In addition, two key human performance issues (trust and situation awareness) related to H-A teaming for multirobot control and some promising user interface design solutions to address these issues were discussed. Some major individual differences factors (operator spatial ability, attentional control ability, and gaming experience) were identified that may impact H-A teaming in the context of robotics control.

Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues

Objective:We investigated the effects of level of agent transparency on operator performance, trust, and workload in a context of human–agent teaming for multirobot management.Background:Participants played the role of a heterogeneous unmanned vehicle (UxV) operator and were instructed to complete various missions by giving orders to UxVs through a computer interface. An intelligent agent (IA) assisted the participant by recommending two plans—a top recommendation and a secondary recommendation—for every mission.Method:A within-subjects design with three levels of agent transparency was employed in the present experiment. There were eight missions in each of three experimental blocks, grouped by level of transparency. During each experimental block, the IA was incorrect three out of eight times due to external information (e.g., commander’s intent and intelligence). Operator performance, trust, workload, and usability data were collected.Results:Results indicate that operator performance, trust, and perce...

https://journals.sagepub.com/doi/pdf/10.1177/0018720815621206

Intelligent Agent Transparency in Human-Agent Teaming for Multi-UxV Management.

Effective collaboration between humans and agents depends on humans maintaining an appropriate understanding of and calibrated trust in the judgment of their agent counterparts. The Situation Aware...

https://www.tandfonline.com/doi/pdf/10.1080/1463922X.2017.1315750

Situation awareness-based agent transparency and human-autonomy teaming effectiveness

: Autonomous agents have been increasingly used for military operations (e.g., casualty extraction, explosive detection and disposals, reconnaissance and surveillance, supply transportation, building clearing, and firefighting), search and rescue, transportation safety, space exploration, farming, and many other purposes yet to emerge (Barnes and Evans, 2010; Greenemeier, 2010; Jones and Schmidlin, 2011; Morals and the Machine, 2012; Murphy and Burke, 2010; Osborn, 2011; Purdy, 2008; Singer, 2010). As these agents become more sophisticated and independent, it is critical for their human counterparts to understand their behaviors, the reasoning process behind those behaviors, and the expected outcomes to properly calibrate their trust in the systems and make appropriate decisions (de Visser et al., 2014; Lee and See, 2004). Indeed, past studies have shown that human operators sometimes question the accuracy and effectiveness of agents actions due to the operators difficulties understanding the state/status of the agent (Bitan and Meyer, 2007; Seppelt and Lee, 2007; Stanton et al., 2007) and the rationales behind the behaviors (Linegang et al., 2006). These limitations can be substantial impediments to overall system and task performance.

/pdf/situation-awareness-based-agent-transparency-4f67krsa66.pdf

Michael J. Barnes

Papers

Human Performance Issues and User Interface Design for Teleoperated Robots

Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues

Intelligent Agent Transparency in Human-Agent Teaming for Multi-UxV Management.

Situation awareness-based agent transparency and human-autonomy teaming effectiveness

Situation Awareness-Based Agent Transparency