Towards Adjustable Autonomy for the Real World
  Adjustable autonomy refers to entities dynamically varying their own
autonomy, transferring decision-making control to other entities (typically
agents transferring control to human users) in key situations. Determining
whether and when such transfers-of-control should occur is arguably the
fundamental research problem in adjustable autonomy. Previous work has
investigated various approaches to addressing this problem but has often
focused on individual agent-human interactions. Unfortunately, domains
requiring collaboration between teams of agents and humans reveal two key
shortcomings of these previous approaches. First, these approaches use rigid
one-shot transfers of control that can result in unacceptable coordination
failures in multiagent settings. Second, they ignore costs (e.g., in terms of
time delays or effects on actions) to an agent's team due to such
transfers-of-control. To remedy these problems, this article presents a novel
approach to adjustable autonomy, based on the notion of a transfer-of-control
strategy. A transfer-of-control strategy consists of a conditional sequence of
two types of actions: (i) actions to transfer decision-making control (e.g.,
from an agent to a user or vice versa) and (ii) actions to change an agent's
pre-specified coordination constraints with team members, aimed at minimizing
miscoordination costs. The goal is for high-quality individual decisions to be
made with minimal disruption to the coordination of the team. We present a
mathematical model of transfer-of-control strategies. The model guides and
informs the operationalization of the strategies using Markov Decision
Processes, which select an optimal strategy, given an uncertain environment and
costs to the individuals and teams. The approach has been carefully evaluated,
including via its use in a real-world, deployed multi-agent system that assists
a research group in its daily activities.
