Yin, Zhengyu (University of Southern California) | Jiang, Albert Xin (University of Southern California) | Tambe, Milind (University of Southern California) | Kiekintveld, Christopher (University of Texas at El Paso) | Leyton-Brown, Kevin (University of British Columbia) | Sandholm, Tuomas (Carnegie Mellon University) | Sullivan, John P. (Los Angeles County Sheriff's Department)
Given that agents are spread across such a network, agents directly interact only with a small group of neighbors. In this approach, agents form groups of one or more agents until no group of k or fewer agents can possibly improve the DCOP solution; we define this type of local optimum, and any algorithm guaranteed to reach such a local optimum, as k-optimal. The first set of results gives worst-case guarantees on the solution quality of k-optima in a DCOP. These guarantees can help determine an appropriate k-optimal algorithm, or possibly an appropriate constraint graph structure, for agents to use in situations where the cost of coordination between agents must be weighed against the quality of the solution reached.
The operation of a human organization requires dozens of everyday tasks to ensure coherence in organizational activities, monitor the status of such activities, gather information relevant to the organization, keep everyone in the organization informed, and so on. Based on this vision, this article reports on ELECTRIC ELVES, a system that has been operational 24 hours a day, 7 days a week at our research institute since 1 June 2000. Tied to individual user workstations, fax machines, voice, and mobile devices such as cell phones and palm pilots, ELECTRIC ELVES has assisted us in routine tasks, such as rescheduling meetings, selecting presenters for research meetings, tracking people's locations, organizing lunch meetings, and so on. We also report the results of deploying ELECTRIC ELVES in our own research organization.
With the growing importance of multiagent team-work, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. ISAAC'S novelty stems from a key design constraint that arises in team analysis: Multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. Additionally, ISAAC uses multiple presentation techniques that can aid human understanding of the analyses. This article presents ISAAC'S general conceptual framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup Scientific Challenge Award.
The Robot World Cup Soccer Games and Conferences (RoboCup) are a series of competitions and events designed to promote the full integration of AI and robotics research. Following the first RoboCup, held in Nagoya, Japan, in 1997, RoboCup-98 was held in Paris from 2-9 July, overlapping with the real World Cup soccer competition. RoboCup-98 included competitions in three leagues: (1) the simulation league, (2) the real robot small-size league, and (3) the real robot middle-size league. Champion teams were cmunited-98 in both the simulation and the real robot small-size leagues and cs-freiburg (Freiburg, Germany) in the real robot middle-size league.
Tambe, Milind, Jung, Hyuckchul
In a complex, dynamic multiagent setting, coherent team actions are often jeopardized by conflicts in agents' beliefs, plans, and actions. CONSA focuses on exploiting the benefits of argumentation in a team setting. Thus, CONSA casts conflict resolution as a team problem, so that the recent advances in teamwork can be brought to bear during conflict resolution to improve argumentation flexibility. Furthermore, because teamwork conflicts sometimes involve past teamwork, teamwork models can be exploited to provide agents with reusable argumentation knowledge.
Tambe, Milind, Gmytrasiewicz, Piotr
Interactive simulation environments constitute one of today's promising emerging technologies, with applications in areas such as education, manufacturing, entertainment, and training. These environments are also rich domains for building and investigating intelligent automated agents, with requirements for the integration of a variety of agent capabilities but without the costs and demands of low-level perceptual processing or robotic control. Our current target is intelligent automated pilots for battlefield-simulation environments. This article provides an overview of this domain and project by analyzing the challenges that automated pilots face in battlefield simulations, describing how TacAir-Soar is successfully able to address many of them -- TacAir-Soar pilots have already successfully participated in constrained air-combat simulations against expert human pilots -- and discussing the issues involved in resolving the remaining research challenges.