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.
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 ), a number of implementations (in the IRMA architecture  and the various PRS-like systems currently available ), 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 ), 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” ) 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
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.
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
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.
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
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.
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
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.
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.
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.