An essential quality of a good partner is her responsiveness to other team members. Recent work in dynamic plan execution exhibits elements of this quality through the ability to adapt to the temporal uncertainties of others agents and the environment. However, a good teammate also has the ability to adapt on-the-fly through task assignment. We generalize the framework of dynamic execution to perform plan execution with dynamic task assignment as well as scheduling. This paper introduces Chaski, a multi-agent executive for scheduling temporal plans with online task assignment. Chaski enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents. The agent then uses the updated plan to choose, schedule and execute actions that are guaranteed to be temporally consistent and logically valid within the multi-agent plan. Chaski is made efficient through an incremental algorithm that compactly encodes all scheduling policies for all possible task assignments. We apply Chaski to perform multi-manipulator coordination using two Barrett Arms within the authors' hardware testbed. We empirically demonstrate up to one order of magnitude improvements in execution latency and solution compactness compared to prior art.

The dispatcher uses the dispatchable form to quickly make dynamic scheduling decisions. As autonomous systems become more capable and common, However, developing flexible executives for plans with they will need to reason about complex tasks and robustly choices, has been more difficult. Kim, Williams, and execute plans in uncertain environments. In previous work, Abramson present an executive called Kirk, which uses a Williams et al. introduced the Reactive Model-Based Programming deliberative planning step to change the execution sequence Language (RMPL), which is designed to allow online (2001). Although their results show improvement engineers to simply and intuitively express the desired behavior over prior planning systems, the latency is still too high for of the system (2003). Then the agent's executive determines tightly coupled systems, for example robots working with the correct sequence of actions to accomplish this humans or walking robots with fast dynamics. Recently, behavior, relieving the programmer of explicitly coding that Shah and Williams extended the compiler and dispatcher logic. RMPL programs often involve temporal constraints model to Temporal Constraint Satisfaction Problems (TCwhich the executives must reason over. SPs), a type of temporal problems with choice, by compactly Kim, Williams, and Abramson previously developed recording the possible set of solutions and efficiently Temporal Plan Networks (TPNs) as a temporal constraint reasoning over the possible options (2008).