In this paper we present a lightweight teamwork implementation by using abstraction hierarchies. The basis of this implementation is ADAPT, which supports Autonomous Dynamic Agent Planning for Teamwork. ADAPT's novelty stems from how it succinctly decomposes teamwork problems into two separate planners: a task network for the set of activities to be performed by a specific agent and a separate group network for addressing team organization factors. Because abstract search techniques are the basis for creating these two components, ADAPT agents are able to effectively address teamwork in dynamic environments without explicitly enumerating the entire set of possible team states. During run-time, ADAPT agents then expand the teamwork states that are necessary for task completion through an association algorithm to dynamically link its task and group planners. As a result, ADAPT uses far fewer team states than existing teamwork models. We describe how ADAPT was implemented within a commercial training and simulation application, and present evidence detailing its success in concisely and effectively modeling teamwork.
['Meirav Hadad', 'Avi Rosenfeld']
ADAPT: abstraction hierarchies to better simulate teamwork under dynamics