Publications by Year: 1997

Milind Tambe. 1997. “Agent architectures for flexible, practical teamwork .” In National Conference on Artificial Intelligence (AAAI-97) .Abstract
Teamwork in complex, dynamic, multi-agent domains mandates highly flexible coordination and communication. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is agent architectures with integrated teamwork capabilities. This fundamental shift in agent architectures is illustrated via an implemented candidate: STEAM. While STEAM is founded on the joint intentions theory, practical operationalization has required it to integrate several key novel concepts: (i) team synchronization to establish joint intentions; (ii) constructs for monitoring joint intentions and repair; and (iii) decision-theoretic communication selectivity (to pragmatically extend the joint intentions theory). Applications in three different complex domains, with empirical results, are presented.1
Milind Tambe. 1997. “Towards Flexible Teamwork .” Journal of Artificial Intelligence Research, 7, Pp. 83-124.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.
R. Hill, J. Chen, J. Gratch, P. S. Rosenbloom, and Milind Tambe. 1997. “Intelligent agents for the synthetic battlefield: A company of rotary wing aircraft.” Innovative Applications of Artificial Intelligence (IAAI-97).Abstract
We have constructed a team of intelligent agents that perform the tasks of an attack helicopter company for a synthetic battlefield environment used for running largescale military exercises. We have used the Soar integrated architecture to develop: (1) pilot agents for a company of helicopters, (2) a command agent that makes decisions and plans for the helicopter company, and (3) an approach to teamwork that enables the pilot agents to coordinate their activities in accomplishing the goals of the company. This case study describes the task domain and architecture of our application, as well as the benefits and lessons learned from applying AI technology to this domain.
H. Kitano, Milind Tambe, P. Stone, M. Veloso, S. Coradeschi, E. Osawa, H. Matsubara, I. Noda, and M. Asada. 1997. “The RoboCup Synthetic Agent Challenge 97.” International Joint Conference on Artificial Intelligence (IJCAI97).Abstract
RoboCup Challenge oers a set of challenges for intelligent agent researchers using a friendly competition in a dynamic, real-time, multi-agent domain: synthetic Soccer. While RoboCup in general envisions longer range challenges over the next few decades, RoboCup Challenge presents three specic challenges for the next two years: (i) learning of individual agents and teams; (ii) multi-agent team planning and plan-execution in service of teamwork; and (iii) opponent modeling. RoboCup Challenge provides a novel opportunity for researchers in planning and multi-agent arenas | it not only supplies them a concrete domain to evalute their techniques, but also challenges them to evolve these techniques to face key constraints fundamental to this domain: real-time and teamwork.