@conference {1510809,
	title = {Teamwork in cyberspace: Using TEAMCORE to make agents team-ready },
	booktitle = {AAAI Spring Symposium on Agents in Cyberspace},
	year = {1999},
	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{\textquoteright}s
unanticipated failure in fulfilling responsibilities, team
members{\textquoteright} 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 {\textquotedblleft}wrapper{\textquotedblright} a
teamcore agent. A teamcore agent is a pure {\textquotedblleft}social agent{\textquotedblright},
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.},
	author = {Tambe, Milind and W. Shen and M. Mataric and D. Goldberg and Pragnesh J. Modi and Z. Qiu and B. Salemi}
}
