Towards flexible teamwork in persistent teams: extended report


Milind Tambe and W. Zhang. 2000. “Towards flexible teamwork in persistent teams: extended report .” Journal of Autonomous Agents and Multi-agent Systems, special issue on 'Best of ICMAS 98,' 3, Pp. 159-183.


Teams of heterogeneous agents working within and alongside human organizations offer exciting possibilities for streamlining processes in ways not possible with conventional software[4, 6]. For example, personal software assistants and information gathering and scheduling agents can coordinate with each other to achieve a variety of coordination and organizational tasks, e.g. facilitating teaming of experts in an organization for crisis response and aiding in execution and monitoring of such a response[5]. Inevitably, due to the complexity of the environment, the unpredictability of human beings and the range of situation with which the multi-agent systems must deal, there will be times when the system does not produce the results it’s users desire. In such cases human intervention is required. Sometimes simple tweaks are required due to system failures. In other cases, perhaps because a particular user has more experience than the system, the user will want to “steer” the entire multi-agent system on a different course. For example, some researchers at USC/ISI, including ourselves, are currently focused on the Electric Elves project ( In this project humans will be agentified by providing agent proxies to act on their behalf, while entities such as meeting schedulers will be active agents that can communicate with the proxies to achieve a variety of scheduling and rescheduling tasks. In this domain at an individual level a user will sometimes want to override decisions of their proxy. At a team level a human will want to fix undesirable properties of overall team behavior, such as large breaks in a visitor’s schedule. However, to require a human to completely take control of an entire multi-agent system, or even a single agent, defeats the purpose for which the agents were deployed. Thus, while it is desirable that the multi-agent system should not assume full autonomy neither should it be a zero autonomy system. Rather, some form of Adjustable Autonomy (AA) is desired. A system supporting AA is able to dynamically change the autonomy it has to make and carry out decisions, i.e. the system can continuously vary its autonomy from being completely dependent on humans to being completely in control. An AA tool needs to support user interaction with such a system. To support effective user interaction with complex multi-agent system we are developing a layered Adjustable Autonomy approach that allows users to intervene either with a single agent or with a team of agents. Previous work has in AA has looked at either individual agents or whole teams but not, to our knowledge, a layered approach to AA. The layering of the AA parallels the levels of autonomy existing in human organizations. Technically, the layered approach separates out issues relevant at different levels of abstraction, making it easier to provide users with the information and tools they need to effectively interact with a complex multi-agent system.
See also: 2000