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`J
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`Encyclopedia of
`Co puter SCIenc‘
`MMMMMMMMMMMMMMMMMMMMMMMMMM
`' FOURTH Eamon
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`EDITORS
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`Encyclopedia of Computer Science
`Fourth Edition
`Edited by Anthony Ralston, Edwin D. Reilly and David Hemmendinger
`
`© Nature Publishing Group, 2000
`
`All rights reserved. No reproduction, copy or transmission of this publication
`may be made without written permission. No part of this publication may be
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`liable to criminal prosecution and civil claims for damages.
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`Published in the United Kingdom by
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`ISBN: 0-333-7?879-0
`Associated companies throughout the world
`http:iiwww.nature.com
`
`British Library Cataloguing in Publication Data
`
`Encyclopedia of Computer Scheme
`1. Computer science-Encyclopedias
`I. Ralston, Anthony ll. Reilly, Edwin D. III. Hemmendinger, David
`
`004, . 03
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`Pubiished in the United States and Canada by
`GROVE'S DICTIONARIES, INC
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`ISBN: 1—561—59248—X
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`Typeset'in the United Kingdom by Aarontype Limited, Bristol.
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`1 1 94 mum-AGENT SYSTEMS
`
`no mouse pad is needed (see Fig. 2]. An optical sensor
`captures images of the work surface at a rate of 1,500
`images per second, and a digital signal processor {DSP)
`translates changes between the images into on-screen
`movements. This technique, called image correlation
`processing, results in smooth, precise pointer move-
`ment. The mouse features a glowing red underside and
`tail light, a scrolling and zooming wheel, and two
`customizable buttons on its left side which facilitate
`
`Internet (q.v.) navigation and other routine tasks.
`
`Bibliogoghy
`1992. Soberanis, P. "Of Mice and Trends,“ CompuServe
`Magazine, 11, 2 {FebruaI‘J}. 29—30.
`1998. White, R. "The Mechanical Mouse," in How Computers
`Work, 4th Ed., 160—161. Indianapolis, IN: Que/Macmillan
`
`Edwin D. Reilly
`
`area, which is also called Distributed Artificial Intelli—
`gence (DAD, focuses on the development of computa-
`tional principles and models for constructing, describe
`ing, and analyzing the patterns of interaction and
`coordination in both large and small agent societies.
`
`Mum-agent systems provide apotentia] model for com-
`puting in the twenty-first century, in which networks of
`interacting, real-time, intelligent agents integrate the
`work of people and machines, and in which the effec-
`tiveness of computational agents in large distributed
`systems is improved by exploiting the efficiencies of
`organized behavior. Application domains in which
`multi-agent system technology is appropriate typically
`have a naturally spatial, functional, or temporal de-
`composition of knowledge and expertise among agents.
`By structuring such applications as a multi-agent
`system rather than as a single agent, the system will
`have some or all of the following advantages:
`
`MULTI-AGENT SYSTEMS
`
`0 Speed-up clue to concurrent processing;
`
`_--For. articles on related 'subjects'see ARTIFICIAL INTELLIGENCE;
`. DISTRlBU'I'ED SYSTEMS; EXPERT SYSTEMS; and HEURISTIC.
`
`0 Less communication required because processing
`is located nearer the source of information;
`
`Matti-agent systems are computational systems in
`which several artificial “agents", which are programs,
`interact or work together over a communications net-
`work to perform some set of tasks jointly or to satisfy
`some set of goals. These systems may consist of
`homogeneous or heterogeneous agents. Examples of
`agents would be ones for detecting and diagnosing
`network problems Occurring on a segment of a local
`area network: for scheduling the activities of a group
`of machines in a workcell on a factory floor; or for
`locating agents that are selling a specific product and
`deciding on what price to pay. Agents may be
`characterized by whether they are benevolent (coop-
`erative) or self-mterested. Cooperative agents work
`toward achieving a set of shared goals, whereas self-
`interested agents have distinct goals but may still
`interact to further their own goals. For example, in a
`manufacturing setting, where agents are responsible
`for scheduling different aspects of the manufacturing
`process, agents in the same manufacturing company
`would behave in a cooperative way, while agents rep-
`resenting two separate companies, where one com-
`pany was outsourcing part of its manufacturing process
`to the other company, would behave in a self~interested
`way. Agents often need to be send-autonomous and
`highly adaptive clue to their "open" operating environ-
`ments, where the configuration and capabilities of
`other agents and network resources change dynami-
`cally. Agent autonomy relates to an agent's ability to
`make its own decisions about what activities to do,
`when to do them, and to whom information should be
`communicated. Scientific research and practice in this
`
`0 More reliability because of the absence of a single
`point of failure;
`
`0 Real-time (qua) responsiveness due to processing,
`sensing, and effecting being collocated;
`
`O Easier system deveIOpment due to the modularity
`produced by dividing the program into agents.
`
`Domains which have used a multi—agent approach
`include: distributed situation assessment (e.g. network
`diagnosis, information gathering, and monitoring on
`the Internet): distributed resource scheduling and
`planning (e.g. factory scheduling, network manage-
`ment); and distributed expert systems (e.g. concurrent
`engineering). A multi-agent approach is also useful in
`applications in which agents represent the interests of
`different organizational entities (for example, in elec-
`tronic commerce {q.v.) where agents representing the
`interests of diiferent buyers and sellers negotiate over
`an acceptable price for delivery of goods or services).
`Other emerging uses of multi-agent systems are in
`layered systems architectures, in which agents at dif—
`ferent layers need to coordinate their decisions (e.g. to
`achieve appmpriate configurations of resources and
`computational processing} and in the design of resilient
`systems in which agents dynamically reorganize to
`respond to changes in resource availability, software
`and hardware malfunction, and intrusions. In general.
`multi—agent systems provide a framework in which
`both the distribution of processing and information in
`an application and the complexities that come from
`issues of scale can be handled in a natural way.
`
`
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`MULTl—AGENT SYSTEMS
`
`‘l 1 9.5
`
`Agents in such systems need to interact because they
`are solving subproblems that are interdependent,
`either through contention for resources or through
`relationships among the subproblems. This need for
`interaction may require them to cooperate extensively
`during problem—solving based on reasoning about
`subproblem interdependencies,
`the agents' current
`state of problem-solving, and the status of network
`resources. Such agent interactions are exemplified in
`a recently developed commercial multi-agent system
`for restoring service in an electricity transportation
`grid. This application has agents for fault detection,
`fault
`isolation and diagnosis, and network recon-
`figuration. Consider the example of two expert agents
`in this system perforating dilferent forms of fault diag~
`nosis. Each of these agents, operating concurrently,
`uses very different algorithms to do its diagnosis and
`the information that they use is not identical. Both can
`make mistakes, but generally will not make the same
`mistake. They interact by exchanging partial results to
`focus their local diagnostic search processes tawards
`promising areas of the grid where the fault probably
`originated, and away from unpromising ones. They
`also exchange final results to increase the confidence
`in the eventual diagnosis that they agree to. Thus, by
`working together, they not only produce a solution in
`which they have more confidence, but
`they also
`accomplish the task quicker.
`'
`
`Mule—agent systems must be designed to enable an
`agent to modify its problem-solving activity in response
`to the emerging state of the group problemsolving
`effort. Agents must be flexible, as they work with in-
`formation of varying degrees of completeness and
`accuracy, and use resources of varying capabilities.
`For example,
`in the multi~agent system described
`above the agents doing the diagnosis should be able
`to work in a standalone manner, but also be able to
`take advantage of information from the other diag
`nostic agent if and when it arrives. In other words,
`hard-coded assumptions
`about
`information and
`resources are typically avoided. This flexibility requires
`agent autonomy and is in direct contrast to the less
`autonomous characteristics of agents in usual distrib-
`uted processing applications.
`
`The design, implementation, and assessment of multi-
`agent systems raises many specific issues. The major
`conceptual problem that researchers face in dealing
`with these issues is the possibility that the information
`an agent is using to make its decisions is incomplete,
`out~of-date or inconsistent with that of other agents.
`Obtaining all the appropriate non-local information is
`often not practical due to:
`
`1. Limited communication bandwidth (gun) and com-
`putational capabilities which make it infeasible to
`
`transfer, package, and assimilate pertinent infor-
`mation in a timely manner.
`
`2. The heterogeneity of agents, which makes it diffi-
`cult to share information and the possibility that
`compefitive agents, out of self-interest, are not will-
`ing to share certain information.
`
`3. The dynamic character of the environment due to
`changing problems, agents, and resources, and the
`inability to Preclict with certainty the outcome of
`agents’ actions.
`
`In order to deal with this uncertainty in problem-
`solving and coordination decisions, a number of formal
`and heuristic techniques have been developed. These
`techniques are oriented towards achieving effective,
`though not necessarily optimal, agent problem-solving
`and interaction. while limiting the computational and
`communication requirements. These include: group
`problem-solving strategies that can reach acceptable
`solutions even though an individual agent's local
`information may be incorrect or incomplete; coordina-
`tion strategies that enable groups of agents to solve
`problems effectively through decisions about which
`' agents should perform specific tasks and when, and to
`whom they should communicate the results of task
`execution; negotiation mechanisms
`that serve to
`bring a collection of agents to an acceptable state; and
`protocols (q.v.) by which agents may communicate
`and reason about interagent communications. Where
`formal techniques have been used,
`they have gen-
`erally been based on game-theoretic ideas, market
`mechanisms, or logical formalisms, while heuristic
`approaches have their roots in knowledge-based AI
`search, planning and scheduling mechanisms. A recent
`trend is the use of machine learning (q.v.} to acquire the
`information necessary to implement these approaches.
`
`The use of multi—agent systems technology is still in its
`infancy. There are only a handful of commercial appli-
`cations ‘to date. However, given the great interest in the
`field, the emerging multi-agent application develop-
`ment
`infrastructures, and the next generation of
`sophisticated network applications beginning to take
`shape, We may expect
`the impact of multi-agent
`systems on computer science to increase significantly
`during the next decade.
`
`W
`
`1988. Bond, A., and Gasser, L. {eds} Readings in Distributed
`Artificial Intelligence. San Francisco: Morgan Kaufmann.
`1994. Rosenschein, J. 8., and Zlotkin, G. Rules of Encounter:
`Designing Conventions for Automated Negotiation among
`Computers (eds. M. Brady, D, Bobrow and R. Davis).
`Cambridge, MA: MIT Press.
`1994. Jennings, N. R. Cooperation in Industrial Mufti—Agent
`Systems. Singapore: World Scientific.
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`1 1 96 MULTIMEDIA
`
`1995. Proceedings of the First International Conference on
`Matti-agent Systems. San Francisco: AAA] Press.
`1995.0'Hare, G., and Jennings, N. R. (eds) Foundations of
`Distributed Artificial Intelligence. New York: Wiley
`Inter-Science.
`1996. Ptoceedings of the Second International Conference on
`Mufti-agent Systems. Kyoto, Japan: AAAI Press.
`Victor R. Lesser
`
`MULTIMEDIA
`
`For articles on related tepics see ASYNCH RONOUS TRANSFER
`MODE,- COMPUTER—ASSISTED LEARNING AND TEACHING;
`COMPUTER CONFERENCING; COMPUTER CAMS; DarA
`COMMUNICATIONS; DATA COMPRESSION; ENTERTAINMENT
`_ INDUSTRY, COMPUTERS IN THE; HYPERTEKI’; IMAGE
`COMPRESSION; SPEECH RECOGNFFION AND S'NTHBIS;
`VtDEOGAMES; VIRTUAL REALITY; WORKSTATION; and WORLD
`WIDE WEB.
`'
`
`In the context of computing, multimedia has come to
`imply the integration of audio, video, and images with
`more traditional types of data such as text and numer-
`ics. It is an application-oriented technology that caters
`to the multisensory nature of humans and is based on
`the evolving ability of computers to store, transmit,
`and convey diverse types of information.
`
`Multimedia computing is defined as the manipulation
`and presentation of such media in a computer system.
`There are applications of computing in many areas,
`including business, education, manufacturing,
`law,
`medicine, and entertaim-nent, that were inconceivable
`prior to the introduction of this technology. Table 1
`shows a few application domains and their media com-
`
`Table 1. Multimedia and some applications.
`
`Audio, video, imogm, text
`Advertising
`Consumer catalogs
`Educationttraining
`Electronic collaboration
`Electronic mail
`Sales agents
`Tourist information
`
`Images, text
`Dictionaries
`
`Legal information systems
`Library
`Newsprint publication
`
`Images, text, numeric doto
`Geography
`Weather
`Office automation
`Banking
`Engineering, CADI'CAM
`
`Telephony (with other media)
`Command and control
`
`Medical information systems
`———..—__
`
`ponents. Increasingly, these capabiiities are the norm
`as more and more computer applications become
`multimedia applications and multimedia computing is
`absorbed by mainstream computing.
`
`The remainder of this article describes the essence of
`
`multimedia computing and the computer and not
`working components required to support multimedia
`applications.
`'
`
`Representative Applications
`
`The following four areas exemplify multimedia appli
`cations.
`
`ONLINE NEWS
`
`Online news is the multimedia analog of the printed
`newspaper. Through a Web browser, a reader can
`browse through pages of a newspaper, read articles,
`and view pictures or audio/video presentations, as
`shown in Fig. 1. In addition, the user can perform index
`or relational database (q.v.) searches or queries to
`locate specific articles or advertisements. The user may
`also participate in chat groups and provide feedback to
`the editors. The presentation requires synchronization
`among media—the text, image, audio, and video ele-
`ments. Other requirements of this application include
`the ability to format the data for display (e.g. fonts,
`panning, zooming, sequence control (stopping and
`starting of streaming video), and database navigation).
`
`DISTANCE EDUCATION
`Distance education enables students at remote loca-
`tions to participate in live instruction via video con
`ferencing; to collaborate on projects through shared
`"Whiteboards"; Or to replay instructional material that
`has been pro-recorded or pre-orchestrated. Fig. 2 illus-
`trates an example of a multimedia distance learning
`application using the Web as a basis. In this example a
`student can browse through a database consisting of
`course material in various formats (images, audio and
`video recordings, and textual information). Alterna-
`tively, the student can issue queries to the database
`while reading text or viewing illustrations and audio,’
`video presentations.
`
`INTERACTIVE GAMING
`
`the greatest
`Interactive games present perhaps
`demands on the multimedia delivery system due to
`the requirement for real-time, three-dimensional im-
`
`aging coupled with interactions among multiple
`players. SwineOnline (Fig. 3)
`is an example of a
`Webenabled game involving the raising of pigs by
`participants in a virtual state fair. Each participant is
`reSponsible for interacting with and nurturing the
`virtual pct as its Weight increases. A characteristic of
`this application is the need for low-latency interactions
`and support for a large number of interacting players.
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