Improving adjustable autonomy strategies for time-critical domains

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 bo...

• Type: Conference
• Year: 2009
• Where: ATAL
• Authors: Nathan Schurr, Janusz Marecki, Milind Tambe
• Keywords: Adjustable Autonomy, Adjustable Autonomy Algorithms, Artificial Intelligence, ATAL 2009, Decision Making