TOWARDS FLEXIBLE TEAMWORK

Many AI researchers are today striving to build agent teams for comple x, dynamic multi-agent domains, with intended applications in arenas s uch as education, training, entertainment, information integration, an d collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team member s often encounter differing, incomplete, and possibly inconsistent vie ws of their environment. Furthermore, team members can unexpectedly fa il in fulfilling responsibilities or discover unexpected opportunities . Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause seve re failures in teamwork, and their domain-specificity hinders reusabil ity. Our central hypothesis is that the key to such flexibility and re usability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and comm unication, providing requisite flexibility. Furthermore, the models en able reuse across domains, both saving implementation effort and enfor cing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in ST EAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM i s based on agents' building up a (partial) hierarchy of joint intentio ns (this hierarchy is seen to parallel Grosz & Kraus's partial Shared- Plans, 1996). Furthermore, in STEAM, team members monitor the team's a nd individual members' performance, reorganizing the team as necessary . Finally, decision-theoretic communication selectivity in STEAM ensur es reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes ST EAM's application in three different complex domains, and presents det ailed empirical results.