Interleaving real-time multi-agent planning and execution: an application

When faced with real-world planning problems-such as generating plans for simultaneous execution by several interacting agents in a changing environment-traditional approaches to planning fail to provide direct answers. Moreover, if optimal or near optimal solutions are demanded, the complexity involved increases drastically. However certain well-known AI planning techniques (e.g., hierarchical planning, interleaving planning and execution) provide an adequate framework for developing successful applications. This paper is about such an application-how to efficiently plan tasks related with the simultaneous movements of five grippers and a carousel in a robotic system-ensuring a near optimal performance of the overall system