Revisiting Asimov's First Law: A Response to the Call to Arms

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'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' 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.