Improving adjustable autonomy strategies for time-critical domains

As agents begin to perform complex tasks alongside humans as col-laborative teammates, it becomes crucial that the resulting human-multiagent teams adapt to time-critical domains. In such domains, adjustable autonomy has proven useful by allowing for a dynamic transfer of control of decision making between human and agents. However, existing adjustable autonomy algorithms commonly dis-cretize time, which not only results in high algorithm runtimes but also translates into inaccurate transfer of control policies. In ad-dition, existing techniques fail to address decision making incon-sistencies often encountered in human multiagent decision making. To address these limitations, we present novel approach for Resolv-ing Inconsistencies in Adjustable Autonomy in Continuous Time (RIAACT) that makes three contributions: First, we apply contin-uous time planning paradigm to adjustable autonomy, resulting i