@conference {1510288,
	title = {Adjustable autonomy for the real world },
	booktitle = {AAAI Spring Symposium on Safe learning agents},
	year = {2002},
	abstract = {Adjustable autonomy refers to agents{\textquoteright} dynamically varying
their own autonomy, transferring decision making control to
other entities (typically human users) in key situations. Determining whether and when such transfers of control must
occur is arguably the fundamental research question in adjustable autonomy. Previous work, often focused on individual agent-human interactions, has provided several different
techniques to address this question. Unfortunately, domains
requiring collaboration between teams of agents and humans
reveals two key shortcomings of these previous techniques.
First, these techniques 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 of actions) to an agent{\textquoteright}s team due to
such transfers of control.
To remedy these problems, this paper presents a novel approach to adjustable autonomy, based on the notion of transfer of control strategy. A transfer of control strategy consists
of a sequence of two types of actions: (i) actions to transfer
decision-making control (e.g., from the agent to the user or
vice versa) (ii) actions to change an agent{\textquoteright}s pre-specified coordination constraints with others, 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. These strategies are operationalized using Markov Decision Processes to select the optimal strategy
given an uncertain environment and costs to individuals and
teams. We present a detailed evaluation of the approach in
the context of a real-world, deployed multi-agent system that
assists a research group in daily activities.},
	author = {Tambe, Milind and Paul Scerri and D. V. Pynadath}
}
