As agents begin to perform complex tasks alongside humans as collaborative teammates, it becomes crucial that the resulting humanmultiagent 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 discretize time, which not only results in high algorithm runtimes but
also translates into inaccurate transfer of control policies. In addition, existing techniques fail to address decision making inconsistencies often encountered in human multiagent decision making.
To address these limitations, we present novel approach for Resolving Inconsistencies in Adjustable Autonomy in Continuous Time
(RIAACT) that makes three contributions: First, we apply continuous time planning paradigm to adjustable autonomy, resulting in
high-accuracy transfer of control policies. Second, our new adjustable autonomy framework both models and plans for the resolving of inconsistencies between human and agent decisions. Third,
we introduce a new model, Interruptible Action Time-dependent
Markov Decision Problem (IA-TMDP), which allows for actions
to be interrupted at any point in continuous time. We show how
to solve IA-TMDPs efficiently and leverage them to plan for the
resolving of inconsistencies in RIAACT. Furthermore, these contributions have been realized and evaluated in a complex disaster
response simulation system.