of MASs are that (1) each agent has incomplete information or
capabilities for solving the problem and, thus, has a limited
viewpoint; (2) there is no system global control; (3) data are
decentralized; and (4) computation is asynchronous. - Katia Sycar
The study of multiagent
systems (MAS) focuses on systems in which many intelligent agents interact
with each other. The agents are considered to be autonomous entities,
such as software programs or robots. Their interactions can be either
cooperative or selfish. That is, the agents can share a common goal
(e.g. an ant colony), or they can pursue their own interests (as in
the free market economy).
develop communications languages, interaction protocols, and agent architectures
that facilitate the development of multiagent systems. For example,
a MAS researcher can tell you how to program each ant in a colony in
order to get them all to bring food to the nest in the most efficient
manner, or how to set up rules so that a group of selfish agents will
work together to accomplish a given task. MAS researchers draw on ideas
from many disciplines outside of AI, including biology, sociology, economics,
organization and management science, complex systems, and philosophy.
Places to Start
The Smartest Agents
Will Learn to Be Team Players. By Christopher Locke. Red Herring
(January 9, 2002). "An example of how the system would work is a crisis
situation -- say, planning the evacuation of American citizens from
a foreign city during a conflict. In this scenario, each agent in a
multi-agent team would be assigned a specific task, like plotting an
evacuation route, forecasting the weather, or laying out a flight plan.
All of the agents would cooperate and gather data to determine the optimal
Systems. By Katia Sycara (1998). AI Magazine 19(2). Discusses the
need for multiple agent systems communicating peer-to-peer.
Systems: An Emerging Subdiscipline of AI. Victor R. Lesser (1995).
ACM Computing Surveys, 27 (3): 340 -342. "As more AI applications are
being formulated in terms of spatially, functionally, or temporally
distributed processing, multiagent systems (or what was previously called
distributed AI) are emerging as an important subdiscipline of AI. ...
In general multiagent systems are computational systems in which several
semi-autonomous agents interact or work together to perform some set
of tasks or satisfy some set of goals."
of Multiagent Systems. From the Xerox Palo Alto Research Center.
"Multiagent systems arise in human societies, biological ecosystems,
the immune system and distributed computation. While very different
in detail they all face the issue of producing complex global behavior
through the local interactions of their constituent parts. This is particularly
problematic since the individual parts have only a limited view of the
system as a whole." Topics include Dynamics of Cooperation in Societies,
Computational Societies and Economies, and Agent-Based Control of Smart
Matter, and they offer lots of papers for those who want to explore
these areas in depth.
MultiAgent Systems. Maintained by
Jose Vidal, professor in Electronic and Computer Engineering, University
of South Carolina. A wealth of links to a variety of resources.
of Multi-Agent System Frameworks. By Roberto A. Flores-Mendez. Well-written,
To find out what
& Demons have to do with MAS, see our Namesakes
Journal of Artificial Societies
and Social Simulation, "an inter-disciplinary journal for the exploration
and understanding of social proceses by means of computer simulation."
SIGMA: Multiagent Learning
and Adaptation in an Information Filtering Market. By Innes A. Ferguson
and Grigoris J. Karakoulas. The Interactive Information Group at the
Institute for Information Technology, National Research Council, Ottawa
ON. "By augmenting a particular collection of these IF agents with learning
and adaptation techniques - including reinforcement learning, bidding
price adjustment, and relevance feedback - we have created a robust
network-based application which adapts to both changes in the characteristics
of the information available on the network, as well as to changes in
individual users' information delivery requirements."
Systems: 1994 AAAI Presidential Address. By Barbara Grosz. 1996.
AI Magazine 17(2), 67-85.
on Intelligent Agents and Multiagent Systems. By Vasant Honavar,
Department of Computer Science, Iowa State University.
Game Theory. Daphne
Koller's article for the MIT Encyclopedia of Cognitive Science. "Game
theory is a mathematical framework designed for analyzing the interaction
between several agents whose decisions affect each other. In a game-theoretic
analysis, an interactive situation is described as a game: an abstract
description of the players (agents), the courses of actions available
to them, and their preferences over the possible outcomes." In addition
to her overview, you'll find references, readings, and links to other
agents ask for help. By Kimberly Patch, Technology Research News
(September 18/25, 2002). "If you're good at something, people naturally
ask your advice about it. Researchers from the University of Porto in
Portugal are tapping this learning strategy by programming tiny bits
of software, called agents, to ask other agents for help as the group
figures out how to control the timing of traffic lights."
Backs of Ants - New networks mimic the behavior of insects and bacteria.
By Kimberly Patch. Technology Review (March 19, 2003). "Drawing heavily
on the chemistry of biology, researchers from Humboldt University in
Germany have devised a way for electronic agents to efficiently assemble
a network without relying on a central plan."
Swarms - Ant teamwork suggests models for computing faster and organizing
better. By Ivars Peterson. Science News, Vol. 158, No. 20 (November
11, 2000). "In effect, astonishing feats of teamwork emerge from a large
number of unsupervised individuals following a few simple rules. This
sort of self-organizing cooperative behavior among ants, bees, and other
social insects has become the envy of engineers and computer scientists
as they work to solve tough path-finding, scheduling, and control problems
in industrial and other settings. In recent years, studies of ant behavior
have suggested powerful computational methods for finding alternative
traffic routes over congested telephone lines and novel algorithms for
governing how robots operating independently would work together."
Corners. By Jonathan Rauch. The Atlantic (April 2002). "The new
science of artificial societies suggests that real ones are both more
predictable and more surprising than we thought. Growing long-vanished
civilizations and modern-day genocides on computers will probably never
enable us to foresee the future in detail -- but we might learn to anticipate
the kinds of events that lie ahead, and where to look for interventions
that might work."
Systems: A Survey from a Machine Learning Perspective. By Peter
Stone and Manuela Veloso, Computer Science Department, Carnegie Mellon
University. "Distributed Artificial Intelligence (DAI) has existed as
a subfield of AI for less than two decades. DAI is concerned with systems
that consist of multiple independent entities that interact in a domain.
Traditionally, DAI has been divided into two sub-disciplines: Distributed
Problem Solving (DPS) focusses on the information management aspects
of systems with several branches working together towards a common goal;
Multiagent Systems (MAS) deals with behavior management in collections
of several independent entities, or agents. This survey of MAS is intended
to serve as an introduction to the field and as an organizational framework."
computational economics (ACE) is the computational study of economies
modelled as evolving systems of autonomous interacting agents. ACE is
thus a specialization to economics of the basic complex adaptive systems
paradigm." Maintained by Leigh Tesfatsion, Department of Economics,
Iowa State University. The site offers links to a variety of helpful
Agent Lab Homepage. "The
goal of the AFIT Lab is to bring together researchers with various expertise
to solve interesting problems in the area of multi-agent systems development
and agent security."
Analysis of Social and Organizational Systems. "Gourps, organizations,
and societies are inherently computational and computational multi-agent
systems are inherently organizational. Thus, within CASOS we attempt
to understand and formally model two distinct but complimentary types
of phenomena. The first is the natural or human group, organizational
or society, which is universally informatted and continually acquires,
manipulates, and produces information (and possibly other material goods)
through the joint, and interlocked activities of people and automated
information technologies. The second is the artificial computational
systems which is generally comprised of multiple distributed agents
who can mutually influence, constrain and suppurt each other as they
try to manage and manipulate the knowledge, communication and interaction
networks in which they are embedded. Computational analysis is used
to develop a better understanding of the fundamental priciples of organizing,
coordinating, and managing multiple information processing agents (whether
they are human, WebBot, or robots) and the fundamental dynamic nature
of groups, organizations and societies." - from their Mission
CORO. Collective Robotics Research
Group at the Center for Neuromorphic Systems Engineering at the California
Institute of Technology. "The use of multiple mobile robots offers significant
advantages over the use of single mobile robots: key features are the
possibility of distributed sensing, distributed action, task dependent
reconfigurability, and the enabling of robustness and system reliability
through redundancy. There are several approaches for controlling systems
of multiple mobile robots: most of them, rooted in the conventional
artificial intelligence paradigm, base on complex robots that build
and maintain internal models relevant to the task, and communicate only
explicitly with other robots and external agents including humans. In
the CORO group, we focus instead on the application of Swarm Intelligence
principles, where local interactions among robots and between robots
and environment play a crucial role for achieving the required task."
Control of Agent Based Systems.
Defense Advanced Research Projects Agency (DARPA), U.S. Department of
Defense. Overviews and detailed descriptions of military research projects
Artificial Intelligence links from the Department of Sociology,
University of Surrey.
Robots Project at the Artificial Intelligence Laboratory, Department
of Information Technology, University of Zurich. "Flocking adresses
a variety of important topics in the field of multiagent simulation
and collective robotics which include agent interaction, kin recognition,
and finally the emergence of collective behavior." And their flocking
applet is simply beautiful!
- For related information,
also see: Boids
Network for Social Network Analysis (INSNA)
Learning in Multi-Agent
Systems: Webliography. By M. V. Nagendra Prasad (University of Massachusetts,
Amherst) and Thomas Haynes (University of Tulsa), Links to research
sites, projects, conferences, journals and more.
Systems at The Intell1igent Software Agents Group, Robotics Institute,
Carnegie Mellon University. Be sure to scroll down their page to their
collection of "Applications of Multi-Agent Research."
The Multi-Agent Systems Laboratory
at the Department of Computer Science at the University of Massachusetts
at Amherst. "[This lab] is concerned with the development and analysis
of sophisticated AI problem-solving and control architectures for both
single-agent and multiple-agent systems. The laboratory has pioneered
work in the development of the blackboard architecture, approximate
processing for use in control and real-time AI, and a wide variety of
techniques for coordination of multiple agents."
Open Agent Architecture
- A framework for integrating a community of heterogeneous software
agents in a distributed environment. From SRI's Artificial Intelligence
- Videos of OAA
are also available from SRI. Just scroll down to the "Planning and
Simulation" section in their Video Archive.
Swarm Development Group "Swarm
is a software package for multi-agent simulation of complex systems,
originally developed at the Santa Fe Institute. Swarm is intended to
be a useful tool for researchers in a variety of disciplines. The basic
architecture of Swarm is the simulation of collections of concurrently
interacting agents: with this architecture, we can implement a large
variety of agent based models." Be sure to check out the demos, tutorial,
and the other resources they offer.
Durfee, Edmund H.
1992. What Your Computer Really Needs to Know You Learned in Kindergarten.
In Proceedings of the 10th National Conference on Artificial Intelligence,
858-864. San Jose, CA: AAAI Press.
1998. Multi-Agent Systems: Towards a Collective Intelligence. Reading,
editor. 1995. Proceedings of the First International Conference on Multiagent
Systems. Menlo Park, CA: AAAI Press.
Tokoro, Mario, editor.
1996. Proceedings of the Second International Conference on Multiagent
Systems. Menlo Park, CA: AAAI Press. Topics cover coordination, distributed
planning, implementing multi-agent systems, market-oriented approaches,
multiagent applications, multiagent learning, multiagent search, mutual
knowledge, negotiation, organizational aspects, real-world agents, situated
agents, sociability, and teams of agents.