@conference {1510362,
	title = {Revisiting Asimov{\textquoteright}s First Law: A Response to the Call to Arms },
	booktitle = {Intelligent Agents VIII Proceedings of the International workshop on Agents, theories, architectures and languages (ATAL{\textquoteright}01)},
	year = {2001},
	abstract = {The deployment of autonomous agents in real applications promises
great benefits, but it also risks potentially great harm to humans who interact with
these agents. Indeed, in many applications, agent designers pursue adjustable
autonomy (AA) to enable agents to harness human skills when faced with the
inevitable difficulties in making autonomous decisions. There are two key shortcomings in current AA research. First, current AA techniques focus on individual
agent-human interactions, making assumptions that break down in settings with
teams of agents. Second, humans who interact with agents want guarantees of
safety, possibly beyond the scope of the agent{\textquoteright}s initial conception of optimal AA.
Our approach to AA integrates Markov Decision Processes (MDPs) that are applicable in team settings, with support for explicit safety constraints on agents{\textquoteright}
behaviors. We introduce four types of safety constraints that forbid or require
certain agent behaviors. The paper then presents a novel algorithm that enforces
obedience of such constraints by modifying standard MDP algorithms for generating optimal policies. We prove that the resulting algorithm is correct and present
results from a real-world deployment.},
	author = {D. V. Pynadath and Tambe, Milind}
}
