A study measuring the impact of shared decision making in a human-robot team

Abstract

This paper presents the results of a user study in which the impact of sharing decision making in a human-robot team was measured. In the experiments outlined here, a human and robot play a game together in which the robot searches an arena for items, with input from the human, and the human-robot team earns points for finding and correctly identifying the items. The user study reported here involved 60 human subjects. Each subject interacted with two different robots. With one robot, the human acted as a supervisor: the human issued commands and the robot obeyed. With the other robot, the human acted as a collaborator: the human and robot shared decisions and were required to reach agreement about the robot’s actions in the arena before any actions were taken, facilitated using computational argumentation. Objective performance metrics were collected and analyzed for both types of human-robot team, as well subjective feedback from human subjects regarding attitudes toward working with a robot. The objective results showed significant improvement in performance metrics with the human-as-collaborator pairs versus the human-as-supervisor pairs. Subjective results demonstrated significant differences across many subjective measures and indicated a distinct preference for the human-as-collaborator mode. The primary contribution of this work lies in the demonstration and evaluation of a computational argumentation approach to human-robot interaction, particularly in proving the efficacy of this approach over a less autonomous mode of interaction.

Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature

“…Moreover, participants viewed higher levels of agent autonomy as less burdensome to work with (Johnson et al, 2012) and as requiring a lower workload (Wright et al, 2013, 2018). Finally, humans perceived better collaboration, stronger trust, and increased level of task understanding with greater levels of agent autonomy (e.g., Azhar & Sklar, 2017). A caveat, however, is that other studies by Wright and colleagues found that autonomous agents were better perceived when they provided a moderate level of decision-making input compared to a low or high level (Ruff et al, 2002; Wright & Kaber, 2003, 2005).…”

“…We also note that it may not be surprising that increases in agent autonomy had favorable results, given that 11 of the 15 papers manipulated agent autonomy based on the amount of tasks that the autonomous agent could perform. This can be contrasted with the autonomous agent providing high levels of information exchange and decision support (e.g., Azhar & Sklar, 2017). Table 3 contains additional future research opportunities.…”

“…In four of the studies, the higher levels of agent autonomy involved more decision-making and information exchange. For example, Azhar and Sklar (2017) report on a comparison between "human-as-supervisor" and "human-as-collaborator." In the former, decisions are made by the human, whereas in the latter computational argumentation-based dialog is employed to share decisions.…”

Co-creating Knowledge with Robots: System, Synthesis, and Symbiosis

“…Here, knowledge is based on subjective interpretation (Dodd et al, 2005;Sanzogni et al, 2017), and exists in people and their practices, networks, interactions, and epistemic cultures. Thus, humans and robots can form interactive collaborative teams (Azhar & Sklar, 2017). Information is exchanged from human to robot and from robot to human as indicated in "dynamic interaction" in human-nonhuman collaborative teams (Goodrich & Schultz, 2008, pp.…”

Building Trust in Human-Machine Partnerships

“…The major challenge here is generating the kinds of explanation and probing dialogue that human users will require from the system. From previous work [10], we know that providing argumentation-based explanations can improve the solutions found by some kinds of human-machine team. The question is whether the kinds of arguments that we can construct to explain plans are considered adequate.…”