On June 12-14, 2012, the Board on Global Science and Technology held an international, multidisciplinary workshop in Washington, D.C., to explore the challenges and advances in intelligent human-machine collaboration (IH-MC), particularly as it applies to unstructured environments. This workshop convened researchers from a range of science and engineering disciplines, including robotics, human-robot and human-machine interaction, software agents and multi-agent systems, cognitive sciences, and human-machine teamwork. Participants were drawn from research organizations in Australia, China, Germany, Israel, Italy, Japan, the Netherlands, the United Arab Emirates, the United Kingdom, and the United States.
On day one of the workshop, participants worked in small, interdisciplinary groups to determine how advances in IH-MC over the next two to three years could be applied to solving a variety of different real-world scenarios in dynamic unstructured environments, ranging from managing a natural disaster to improving small-lot agile manufacturing.
On day two, participants organized into small groups for a “deeper dive” exploration of four research topics that had arisen during the scenario discussions. Later in the afternoon, the full group discussed IH-MC in terms of common challenges, hoped-for breakthroughs, and the national, transnational, and global context in which this research occurs.
On day three, participants again organized into small groups to focus on longer term research deliverables. In addition, ten participants gave presentations on their research, with topics ranging from human-robot communication, to disaster response robots, to human-in-the-loop control of robot systems.
Throughout the workshop, participants were not asked to arrive at consensus on any issue but, rather, to identify challenges and opportunities from different disciplinary and cultural perspectives.
What Is Intelligent Human-Machine Collaboration?
Prior to the workshop, participants were asked to give their own definition of “intelligent human-machine collaboration” in a preparatory questionnaire. The following samples show the rich diversity of their responses:
… machines and humans combining each other’s strengths and filling-in for their weaknesses and empowering each other’s capabilities;
… joint and coordinated action by people and computationally based systems, in which each have some stake in the outcome or performance of the mission;
… humans AND machines jointly perform tasks that they would not be able to perform on their own;
… integration of AI into machines;
… humans and machines are able to mutually adapt their behavior, intentions, and communications;
… cooperation that mimics interactions between two humans;
… naturalness of the observed human-machine interaction;
… neither human nor machine treats the other as a disturbance to be minimized.
… machines being partners, and not a tool, for humans;
… technology that amplifies and extends human abilities to know, perceive, and collaborate;
… better overall performance of the mission, independently of how it was achieved;
… shared responsibility, authority, goals.
