Modeling humans as peers and supervisors in computing systems through runtime models

There is a growing demand for more effective integration of humans and computing systems, specifically in multiagent and multirobot systems. There are two aspects to consider in human integration: (1) the ability to control an arbitrary number of robots (particularly heterogeneous robots) and (2) integrating humans as peers in computing systems instead of being just users or supervisors. With traditional supervisory control of multirobot systems, the number of robots that a human can manage effectively is between four and six [17]. A limitation of traditional supervisory control is that the human must interact individually with each robot, which limits the upper-bound on the number of robots that a human can control effectively. In this work, I define the concept of "organizational control" together with an autonomous mechanism that can perform task allocation and other low-level housekeeping duties, which significantly reduces the need for the human to interact with individual robots. Humans are very versatile and robust in the types of tasks they can accomplish. However, failures in computing systems are common and thus redundancies are included to mitigate the chance of failure. When all redundancies have failed, system failure will occur and the computing system will be unable to accomplish its tasks. One way to further reduce the chance of a system failure is to integrate humans as peer "agents" in the computing system. As part of the system, humans can be assigned tasks that would have been impossible to complete due to failures.