Publications by Year: 1999

S. Marsella, J. Adibi, Y. Alonaizan, Gal Kaminka, I. Muslea, and Milind Tambe. 1999. “On being a teammate: Experiences acquired in the design of Robocup teams .” In International conference on Autonomous agents, Agents '99.Abstract
tract Increasingly multiagent systems are being designed for a variety of complex dynamic domains Eective agent inter actions in such domains raise some of the most fundamental research challenges for agentbased systems in teamwork multiagent learning and agent modelling The RoboCup research initiative particularly the simulation league has been proposed to pursue such multiagent research chal lenges using the common testbed of simulation soccer De spite the signicant popularity of RoboCup within the re search community general lessons have not often been ex tracted from participation in RoboCup This is what we attempt to do here We have elded two teams ISIS and ISIS in RoboCup competitions These teams have been in the top four teams in these competitions We compare the teams and attempt to analyze and generalize the lessons learned This analysis reveals several surprises pointing out lessons for teamwork and for multi-agent learning.
M. Georgeff, B. Pell, M. Pollack, Milind Tambe, and M. Wooldrige. 1999. “The Belief-Desire-Intention model of agency .” In Agents, Theories, Architectures and Languages (ATAL).Abstract
Within the ATAL community, the belief-desire-intention (BDI) model has come to be possibly the best known and best studied model of practical reasoning agents. There are several reasons for its success, but perhaps the most compelling are that the BDI model combines a respectable philosophical model of human practical reasoning, (originally developed by Michael Bratman [1]), a number of implementations (in the IRMA architecture [2] and the various PRS-like systems currently available [7]), several successful applications (including the now-famous fault diagnosis system for the space shuttle, as well as factory process control systems and business process management [8]), and finally, an elegant abstract logical semantics, which have been taken up and elaborated upon widely within the agent research community [14, 16]. However, it could be argued that the BDI model is now becoming somewhat dated: the principles of the architecture were established in the mid-1980s, and have remained essentially unchanged since then. With the explosion of interest in intelligent agents and multi-agent systems that has occurred since then, a great many other architectures have been developed, which, it could be argued, address some issues that the BDI model fundamentally fails to. Furthermore, the focus of agent research (and AI in general) has shifted significantly since the BDI model was originally developed. New advances in understanding (such as Russell and Subramanian’s model of “boundedoptimal agents” [15]) have led to radical changes in how the agents community (and more generally, the artificial intelligence community) views its enterprise. The purpose of this panel is therefore to establish how the BDI model stands in relation to other contemporary models of agency, and in particular where it can or should go next.
Milind Tambe and H. Jungh. 1999. “The benefits of arguing in a team .” AI Magazine, Winter 1999 20 (4).Abstract
In a complex, dynamic multi-agentsetting, coherent team actions are often jeopardized by conflicts in agents’ beliefs, plans and actions. Despite the considerable progress in teamwork research, the challenge ofintra-team conflict resolutionhas remained largely unaddressed. This paper presents CONSA, a system we are developing to resolve conflicts using argumentation-based negotiations. CONSA is focused 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, since teamwork conflicts sometimes involve past teamwork, teamwork models can be exploited to provide agents with reusable argumentation knowledge. Additionally, CONSA also includes argumentation strategies geared towards benefiting the team rather than the individual, and techniques to reduce argumentation overhead.
Gal Kaminka and Milind Tambe. 1999. “I'm OK, You're OK, We're OK: Experiments in Centralized and Distributed Socially Attentive Monitoring .” In International conference on Automonomous Agents, Agents 99.Abstract
Execution monitoring is a critical challenge for agents in dynamic, complex, multi-agent domains. Existing approaches utilize goalattentive models which monitor achievement of task goals. However, they lack knowledge of the intended relationships which should hold among the agents, and so fail to address key opportunities and difficulties in multi-agent settings. We explore SAM, a novel complementary framework for social monitoring that utilizes knowledge of social relationships among agents in monitoring them. We compare the performance of SAM when monitoring is done by a single agent in a centralized fashion, versus team monitoring in a distributed fashion. We experiment with several SAM instantiations, algorithms that are sound and incomplete, unsound and complete, and both sound and complete. While a more complex algorithm appears useful in the centralized case (but is unsound), the surprising result is that a much simpler algorithm in the distributed case is both sound and complete. We present a set of techniques for practical, efficient implementations with rigorously proven performance guarantees, and systematic empirical validation.
D. V. Pynadath, Milind Tambe, and N. Chauvat. 1999. “Rapid integration and coordination of heterogeneous distributed agents for collaborative enterprises .” In DARPA JFACC symposium on advances in Enterprise Control.Abstract
As the agent methodology proves more and more useful in organizationalenterprises, research/industrial groups are developing autonomous, heterogeneous agents that are distributed over a variety of platforms and environments. Rapid integration of such distributed, heterogeneous agent components could address large-scale problems of interest in these enterprises. Unfortunately, rapid and robust integration remains a difficult challenge. To address this challenge, we are developing a novel teamwork-based agent integration framework. In this framework, software developers specify an agent organization through a team-oriented program. To locate and recruit agent components for this organization, an agent resources manager (an analogue of a “human resources manager”) searches for agents of interest to this organization and monitors their performance over time. TEAMCORE wrappers render the agent components in this organization team ready, thus ensuring robust, flexible teamwork among the members of the newly formed organization. This implemented framework promises to reduce the development effort in enterprise integration while providing robustness due to its teamwork-based foundations. We have applied this framework to a concrete, running example, using heterogeneous, distributed agents in a problem setting comparable to many collaborative enterprises.
Milind Tambe, W. Shen, M. Mataric, D. Goldberg, Pragnesh J. Modi, Z. Qiu, and B. Salemi. 1999. “Teamwork in cyberspace: Using TEAMCORE to make agents team-ready .” In AAAI Spring Symposium on Agents in Cyberspace.Abstract
In complex, dynamic and uncertain environments extending from disaster rescue missions, to future battlefields, to monitoring and surveillance tasks, to virtual training environments, to future robotic space missions, intelligent agents will play a key role in information gathering and filtering, as well as in task planning and execution. Although physically distributed on a variety of platforms, these agents will interact with information sources, network facilities, and other agents via cyberspace, in the form of the Internet, Intranet, the secure defense communication network, or other forms of cyberspace. Indeed, it now appears well accepted that cyberspace will be (if it is not already) populated by a vast number of such distributed, individual agents. Thus, a new distributed model of agent development has begun to emerge. In particular, when faced with a new task, this model prescribes working with a distributed set of agents rather than building a centralized, large-scale, monolithic individual agent. A centralized approach suffers from problems in robustness (due to a single point of failure), exhibits a lack of modularity (as a single monolithic system), suffers from difficulty in scalability (by not utilizing existing agents as components), and is often a mismatch with the distributed ground reality. The distributed approach addresses these weaknesses of the centralized approach. Our hypothesis is that the key to the success of such a distributed approach is teamwork in cyberspace. That is, multiple distributed agents must collaborate in teams in cyberspace so as to scale up to the complexities of the complex and dynamic environments mentioned earlier. For instance, consider an application such as monitoring traffic violators in a city. Ideally, we wish to be able to construct a suitable agent-team quickly, from existing agents that can control UAVs (Unmanned Air Vehicles), an existing 3D route-planning agent, and an agent capable of recognizing traffic violations based on a video input. Furthermore, by suitable substitution, we wish to be able to quickly reconfigure the team to monitor enemy activity on a battlefield or illegal poaching in forests. Such rapid agent-team assembly obviates the need to construct a monolithic agent for each new application from scratch, preserves modularity, and appears better suited for scalability. Of course, such agent teamwork in cyberspace raises a variety of important challenges. In particular, agents must engage in robust and flexible teamwork to overcome the uncertainties in their environment. They must also adapt by learning from past failures. Unfortunately, currently, constructing robust, flexible and adaptive agent teams is extremely difficult. Current approaches to teamwork suffer from a lack of general-purpose teamwork models, which would enable agents to autonomously reason about teamwork or communication and coordination in teamwork and to improve the team performance by learning at the team level. The absence of such teamwork models gives rise to four types of problems. First, team construction becomes highly labor-intensive. In particular, since agents cannot autonomously reason about coordination, human developers have to provide them with large numbers of domain-specific coordination and communication plans. These domain-specific plans are not reusable, and must be developed anew for each new domain. Second, teams suffer from inflexibility. In real-world domains, teams face a variety of uncertainties, such as a team member’s unanticipated failure in fulfilling responsibilities, team members’ divergent beliefs about their environment [CL91], and unexpectedly noisy or faulty communication. Without a teamwork model, it is difficult to anticipate and preplan for the vast number of coordination failures possible due to such uncertainties, leading to inflexibility. A third problem arises in team scale-up. Since creating even small-scale teams is difficult, scaling up to larger ones is even harder. Finally, since agents cannot reason about teamwork, learning about teamwork has also proved to be problematic. Thus, even after repeating a failure, teams are often unable to avoid it in the future. To remedy this situation and to enable rapid development of agent teams, we are developing a novel software system called TEAMCORE that integrates a general-purpose teamwork model and team learning capabilities. TEAMCORE provides these core teamwork capabilities to individual agents, i.e., it wraps them with TEAMCORE. Here, we call the individual TEAMCORE “wrapper” a teamcore agent. A teamcore agent is a pure “social agent”, in that it is provided with only core teamwork capabilities. Given an existing agent with domain-level action capabilities (i.e., the domain-level agent), it is made teamready by interfacing with a teamcore agent. Agents made team-ready will be able to rapidly assemble themselves into a team in any given domain. That is, unlike past approaches such as the open-agent-architecture (OAA) that provides a centralized blackboard facilitator to integrate a distributed set of agents, TEAMCORE is fundamentally a distributed team-oriented system. Our goal is a TEAMCORE system capable of generating teams that are: 1. Flexible and robust, able to surmount the uncertainties mentioned above. 2. Capable of scale-up to hundreds of team members 3. Able to improve the team performance by learning at the team level and avoiding past team failures. An initial version of TEAMCORE system based on the Soar [Newell90] integrated agent architecture is currently up and running. A distributed set of teamcore agents can form teams in cyberspace. The underlying communication infrastructure is currently based on KQML. The rest of this document now briefly describes the TEAMCORE design, architecture and implementation.
D. V. Pynadath, Milind Tambe, N. Chauvat, and L. Cavedon. 1999. “Toward team-oriented programming .” In Agents, theories, architectures and languages (ATAL'99) workshop, to be published in Springer Verlag 'Intelligent Agents VI'.Abstract
t. The promise of agent-based systems is leading towards the development of autonomous, heterogeneous agents, designed by a variety of research/industrial groups and distributed over a variety of platforms and environments. Teamwork among these heterogeneous agents is critical in realizing the full potential of these systems and scaling up to the demands of large-scale applications. Unfortunately, development of robust, flexible agent teams is currently extremely difficult. This paper focuses on significantly accelerating the process of building such teams using a simplified, abstract framework called team-oriented programming (TOP). In TOP, a programmer specifies an agent organization hierarchy and the team tasks for the organization to perform, abstracting away from the innumerable coordination plans potentially necessary to ensure robust and flexible team operation. Our TEAMCORE system supports TOP through a distributed, domain-independent layer that integrates core teamwork coordination and communication capabilities. We have recently used TOP to integrate a diverse team of heterogeneous distributed agents in performing a complex task. We outline the current state of our TOP implementation and the outstanding issues in developing such a framework.
T. Raines, Milind Tambe, and S. Marsella. 1999. “Towards automated team analysis: a machine learning approach .” In Third international RoboCup competitions and workshop.Abstract
Teamwork is becoming increasingly important in a large number of multiagent applications. With the growing importance of teamwork, there is now an increasing need for tools for analysis and evaluation of such teams. We are developing automated techniques for analyzing agent teams. In this paper we present ISAAC, an automated assistant that uses these techniques to perform post-hoc analysis of RoboCup teams. ISAAC requires little domain knowledge, instead using data mining and inductive learning tools to produce the analysis. ISAAC has been applied to all of the teams from RoboCup’97, RoboCup’98, and Pricai’98 in a fully automated fashion. Furthermore, ISAAC is available online for use by developers of RoboCup teams.
Z. Qiu, M. Tambe, and H. Jung. 1999. “Towards flexible negotiation in teamwork .” In Third International Conference on Autonomous Agents (Agents).Abstract
In a complex, dynamic multi-agent setting, coherent team actions are often jeopardized by agents' conflicting beliefs about different aspects of their environment, about resource availability, and about their own or teammates' capabilities and performance. Team members thus need to communicate and negotiate to restore team coherence. This paper focuses on the problem of negotiations in teamwork to resolve such conflicts. The basis of such negotiations is inter-agent argumentation based on Toulmin's argumentation pattern. There are several novel aspects in our approach. First, our approach to argumentation exploits recently developed general, explicit teamwork models, which make it possible to provide a generalized and reusable argumentation facility based on teamwork constraints. Second, an emphasis on collaboration in argumentation leads to novel argumentation strategies geared towards benefiting the team rather than the individual. Third, our goal, to realize argumentation in practice in an agent team, has led to decision theoretic and pruning techniques to reduce argumentation overhead. Our approach is implemented in a system called CONSA.
Milind Tambe, Gal Kaminka, S. Marsella, I. Muslea, and T. Raines. 1999. “Two fielded teams and two experts: A Robocup response challenge from the trenches .” In International joint conference on Artificial Intelligence, IJCAI 99.Abstract
The RoboCup (robot world-cup soccer) effort, initiated to stimulate research in multi-agents and robotics, has blossomed into a significant effort of international proportions. RoboCup is simultaneously a fundamental research effort and a set of competitions for testing research ideas. At IJCAI’97, a broad research challenge was issued for the RoboCup synthetic agents, covering areas of multi-agent learning, teamwork and agent modeling. This paper outlines our attack on the entire breadth of the RoboCup research challenge, on all of its categories, in the form of two fielded, contrasting RoboCup teams, and two off-line soccer analysis agents. We compare the teams and the agents to generalize the lessons learned in learning, teamwork and agent modeling.
Milind Tambe, Jafar Adibi, Yasar Alonaizan, Ali Erdem, Gal Kaminka, Ion Muslea, and Stacy Marsella. 1999. “Building agent teams using an explicit teamwork model and learning.” Artificial Intelligence 110, Pp. 215-240.Abstract

Multi-agent collaboration or teamwork and learning are two critical research challenges in a large number of multi-agent applications. These research challenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research in multi-agent systems. This article describes our approach to address these challenges, based on a team of soccer-playing agents built for the simulation league of RoboCup—the most popular of the RoboCup leagues so far.

To address the challenge of teamwork, we investigate a novel approach based on the (re)use of a domain-independent, explicit model of teamwork, an explicitly represented hierarchy of team plans and goals, and a team organization hierarchy based on roles and role-relationships. This general approach to teamwork, shown to be applicable in other domains beyond RoboCup, both reduces development time and improves teamwork flexibility. We also demonstrate the application of off-line and on-line learning to improve and specialize agents' individual skills in RoboCup. These capabilities enabled our soccer-playing team, ISIS, to successfully participate in the first international RoboCup soccer tournament (RoboCup'97) held in Nagoya, Japan, in August 1997. ISIS won the third-place prize in over 30 teams that participated in the simulation league.