Towards Flexible Teamwork

Abstract:

Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter diering, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fullling responsibilities or discover unexpected opportunities. Highly exible coordination and communication is key in addressing such uncertainties. Simply tting individual agents with precomputed coordination plans will not do, for their inexibility can cause severe failures in teamwork, and their domain-specicity hinders reusability. Our central hypothesis is that the key to such exibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite exibility. Furthermore, the models enable reuse across domains, both saving implementation eort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic com- munication selectivity in STEAM ensures reduction in communication overheads of team- work, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three dierent complex domains, and presents detailed empirical results.