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ckEditors: Michael N. Huhns • huhns@sc .eduMunindar P. Singh • s ingh@ncsu .edu

Michael N. Huhns,Munindar P. Singh,Mark Burstein, Keith Decker,Edmund Durfee,Tim Finin,Les Gasser,Hrishikesh Goradia,Nick Jennings,Kiran Lakkaraju,Hideyuki Nakashima,H.Van Dyke Parunak,Jeffrey S. Rosenschein,Alicia Ruvinsky,Gita Sukthankar,Samarth Swarup,Katia Sycara, Milind Tambe,Tom Wagner,and Laura ZavalaMAS Research Roadmap Project

Research Directions for Service-OrientedMultiagent SystemsToday’s service-oriented systems realize many ideas from the research conducted

a decade or so ago in multiagent systems.Because these two fields are so deeply

connected, further advances in multiagent systems could feed into tomorrow’s

successful service-oriented computing approaches.This article describes a 15-

year roadmap for service-oriented multiagent system research.

We’ve already seen service-orientedcomputing (SOC) take hold incross-enterprise business settings,

such as the use of FedEx and UPS shippingservices in e-commerce transactions; theaggregation of hotel, car rental, and airlineservices by Expedia and Orbitz; or book-rating services for libraries, consumers,and bookstores. Given the widespreadinterest in and deployment of Web servicesand service-oriented architectures that areoccurring in industry, the scope of SOC inbusiness settings will expand substantial-ly. However, the emphasis has been on theexecution of individual services and noton the more important problems of howservices are selected and how they can col-laborate to provide higher levels of func-tionality. Fortunately, four major trends incomputing are addressing this problem:

• Online ontologies are enabling mean-ing and understanding, arguably thelast frontier for computing, to be cap-

tured and shared in more refined ways— via the Semantic Web initiative, forexample, with the development of lan-guages and representations for markingup heterogeneous content. In an alter-native approach, shared representationsare emerging from the works of (mil-lions of) independent content develop-ers. These ontologies will form modelsfor numerous real-world entities andsystems, as well as for the meanings ofdocuments and content.

• Ubiquitous computing, consisting ofwidespread embedded processing withlocal awareness, is making huge stridesin global deployment. It’s expected thatmost of the world’s objects with a dis-tinct identity and exhibiting state orbehavior will have a processor or RFIDtag. The processors themselves consid-er only narrow domains of intelligence— a door, for example, could have aprocessor that knows whether it’s cur-rently locked and under what condi-

tions it should be unlocked. • Entities, from corporations to individuals, will

provide numerous computational behaviors inthe form of Web services and service architec-tures that can be discovered, engaged, andenacted by others.1

• The widespread availability of many differenttypes of sensors and effectors (including actu-ators and robotic devices) will enable onlineentities to not only become aware of the phys-ical world, but also to manipulate, change, andcontrol it.

These trends are the new enablers that will driveSOC and multiagent system (MAS) research in thenext decade and beyond. They portend an era inwhich complex systems will be modeled and simu-lated not just to understand them, but also to formpredictions and interpretations that guide the mon-itoring and managing of them. SOC brings to thefore additional considerations, such as the necessi-ty of modeling autonomous and heterogeneouscomponents in uncertain and dynamic environ-ments. Such components must be autonomouslyreactive and proactive yet able to interact flexiblywith other components and environments. As aresult, they’re best thought of as agents, which col-lectively form MASs. Additionally, the key MASconcepts are reflected directly in those of SOC:

• ontologies (simplified representations of knowl-edge in a domain, developed with the purposeof facilitating interoperation);

• process models (simplified representations ofactivities and their enactment);

• choreography (simplified business protocolsthrough which services can interact);

• directories and facilitators (simplified “middleagents” from MASs); and

• service-level agreements and quality-of-servicemeasures (automated negotiation and flexibleservice execution in dynamic environments).

SOC represents an emerging class of approacheswith MAS-like characteristics for developing sys-tems in large-scale open environments. Indeed, SOCpresents several challenges that can’t be tackledwithout MAS concepts and techniques. Viewed inthis light, MASs offer many ways in which tochange the face of computing.

Multiagent SystemsThe history of MASs mirrors the history of comput-

ing in general. In the 1980s, distributed computingover LANs and advances in expert systems motivat-ed the initial interest in distributed agents. Becausethe resulting systems functioned in single organiza-tions, cooperation was the main focus. In the 1990s,opening LANs to the Internet ushered in an interestin MASs, which have a dynamic topology whoseagents potentially could be implemented and main-tained by more than one organization. The researchfocus shifted to interaction in general, with possi-bilities for emergent behaviors: the global behaviorof a system consisting of many agents emerges fromthe interactions among agents and isn’t always obvi-ous from the agents’ individual behaviors.

The problems that MASs address aren’t new —ancient societies and economies encountered thesame basic challenges of autonomy and hetero-geneity. What is new is how the emergence of theInternet and ubiquitous computing has renderedtraditional, manual approaches ineffective againstsuch challenges, placing these problems squarely inthe realm of modern IT.

MASs have reached a level of maturity in whichthey’re now entrusted with spacecraft control, med-ical-record processing, military mission scheduling,supply-network management, and multimillion-dollar-auction staging. MASs have affected severalother areas as well, including entertainment, edu-cation, training simulations, mechanism design, dis-tributed constraint satisfaction, user interfaces, andagent-based models of social networks and humanorganizations. In spite of these successes, MASshaven’t yet become the mandatory architecturalapproach for information system construction. Fun-damental problems remain, ranging from engineer-ing individual agents to scaling them to open,enterprise-wide applications. Several core MASchallenges, arising in concepts such as autonomy,cooperation, commitments, and joint action, con-tinue to pose challenges.

Elements of an MAS VisionThe roadmap for multiagent-based SOC research,devised at a retreat we attended in May 2005 inSouth Carolina, describes the research challengesthat must be met before developers can produceapplication classes such as pervasive service envi-ronments, society-inspired systems, and computa-tional service mechanisms.

Pervasive Service EnvironmentsBeyond today’s SOC installations, an obvious nextstep is the development of pervasive service envi-

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ronments, in which services are widely available ineveryday home, office, and play environments. Inthe future, independently developed services will bedynamically selected, engaged, composed, and exe-cuted in a context-sensitive manner. Such servicescould support several applications, including

• heterogeneous information management with-in and across enterprises, thereby facilitatingsuperior process management and e-business;

• scientific computing with large, dynamicallyreconfigurable resources (such as in grid com-puting), thereby supporting the solution ofgrander scientific problems;

• mobile computing, in which mobile users obtaindesired information at the right time from fixedor mobile information resources; and

• pervasive computing, in which computationalresources are associated with components ofthe (physical) infrastructure that surrounds us,thereby leading to optimized management ofresources and an improved user experience.

Instead of passively waiting for discovery, servicescould proactively contribute to an application,thereby behaving like agents in an MAS.

Society-Inspired SystemsSocietal representations of large-scale systemsfacilitate the exploration and understanding ofrelationships between elements of our world whosecomplexity has kept them a mystery. Such repre-sentations are appropriate for a vast range of sys-tem domains, including

• global environmental phenomena, such as cli-mate change and extreme weather predictionand detection;

• biological networks, such as tools for epidemi-ological modeling (for example, contagion ofbird flu or mad-cow disease); and

• computational pathologies, such as viruses orspam.

Large-scale simulations inform decision makingby enabling “what if” analyses that help peopleunderstand the consequences of possible military,economic, political, or environmental actions.Understanding the effect of mobile phone usage ontraffic accidents in a given area, for example, couldhelp choose between banning cell phone usagewhile driving and lowering the speed limit. MASsimprove the verisimilitude of such simulations

because agents have more of the same characteris-tics of the entities involved in the simulation.

Computational Service MechanismsAs services become increasingly “alive” and theirinteractions become increasingly dynamic, they’llbegin to do more than just manage information inexplicitly programmed ways. In particular, MASs orservices acting in concert can function as compu-tational mechanisms in their own right, thus signif-icantly enhancing our ability to model, design,build, and manage complex software systems. Thinkof such MASs as providing a new approach for con-structing complex applications wherein developersconcentrate on high-level abstractions, such asoverall behavior and key conceptual structures (theactive entities, their objectives, and their interac-tions), without having to go further into individualagents’ details or interactions.

This vision becomes more compelling as thetarget environments become more

• populous (a monolithic model is intractable,whereas developers can construct an MASmodularly);

• distributed (pulling information to a centrallocation for monitoring and control is prohib-itive, whereas techniques based on interactionamong agents and the emergence of desiredsystem-level behaviors are much easier to man-age); and

• dynamic (an MAS can adapt in real time tochanges in the target system and the environ-ment in which it is embedded).

Ashby’s principle of requisite variety2 states that asystem’s controller must have complexity at leastequal to the system itself, or “every good regulatorof a system must be a model of that system.”3 Fromthis perspective, using an MAS to manage complexdistributed systems isn’t just feasible, but necessary.

Table 1 shows the ways in which MAS proper-ties can benefit complex system engineering. Poten-tial applications and application domains that canalso benefit from an MAS approach include meet-ing scheduling, scientific workflow management,distributed inventory control and supply chains, airand ground traffic control, telecommunications,electric power distribution, water supplies, andweapon systems.

Past as PrologueTo see where MASs are headed (and thus predict

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SOC’s future), we must look at what they canaccomplish today. In agent-based software engi-neering, MASs form the fundamental buildingblocks of software systems, even those systems thatdon’t require agent-like behaviors.4,5 Another suc-cess is in simulation technology: modern MAS sim-ulation platforms can support 104 to 105 concurrentagents. By modeling such a huge number of activecomponents, an MAS-based simulation helps usunderstand system-level behavior in settings withnonlinear interactions among many parts, in whichbehavior generally can’t be understood analytically.

To date, a sizable body of MAS research hasn’tyet made many inroads into conventional systemdevelopment:

• Simulated evolution. Defining individual agentbehaviors that will yield desired system behav-ior is analytically intractable, but methods arematuring by which agents can evolve withrespect to fitness functions, thus reducing theengineering burden.

• Stigmergic methods are communication meth-ods in emergent systems in which the individ-ual parts of the system communicate with eachother by modifying their local environment,such as how ants communicate with each otherby depositing pheromones along their paths.Our understanding of how environmentallymediated interactions among agents can yieldemergent coordination is growing; such inter-actions have the added benefit of enforcinglocality and thus ensuring tractability.

• Consensus software for robustness.6,7 MASshave the potential to revolutionize the way inwhich software is produced, developed, andexecuted — simultaneously improving large-scale, mission-critical, and complex systemreliability. Initial experiments show animprovement in robustness due to redundancy.

• Frameworks for describing and controlling soci-

etal-level computations. Controlling a large-scale MAS requires a distributed means forassessing and managing computation by anagent society. TAEMS (Task Analysis and Envi-ronment Modeling System) provides a mecha-nism that could enable the realization,progress, and achievement of societal-levelgoals by helping agents relate their own tasksto those of other agents in their society via for-mally defined hard and soft relationships.

• Engineering tools and frameworks for manag-ing MAS. The Multi-Agent Survivability Simu-lator (MASS) and Multi-Agent ComputingEnvironment (MACE3J) are successful envi-ronments for designing and simulating society-level MASs.8 These technologies focus onreal-world social variables.

In disciplines such as economics, sociology,and marketing, MASs serve as models for under-standing the behavior of highly populous, distrib-uted, dynamic systems. Decision makers could usethem to design city-wide traffic controls, chooseamong distribution schemes for water and elec-tricity in large utility districts, and manage traderelationships among nations.

ChallengesMAS and SOC researchers must address several chal-lenges to realize the applications we’ve described.We won’t discuss infrastructure challenges herebecause processing and communication capacity, aswell as that of sensors and effectors, improves daily.

The development of large-scale MASs calls fornew engineering methods. Such systems must beself-organizing and support runtime reconfigura-tion and design. Techniques such as evolutionarycomputation or population-based search couldserve as design tools for building MASs.

Interaction should be given primacy in terms ofrepresentation and reasoning. To develop open sys-

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Table 1. Reasons for complex system development based on multiagent systems.

Multiagent system properties Benefits for system developmentAutonomous, objective-oriented behavior; agent-oriented decomposition Autonomous, active functionality that adapts to the user’s needs; reuse of whole

subsystems and flexible interactionsDynamic composition and customization ScalabilityInteraction abstractions; statistical or probabilistic protocols Friction-free software; open systems; interactions among heterogeneous systems; move from

sophisticated and learned e-commerce protocols to dynamic selection of protocolsMultiple viewpoints, negotiation, and collaboration Robustness and reliabilitySocial abstractions High-level modeling abstractions

tems, we must capture interactions independently ofthe components that might ultimately perform thoseinteractions. Along these lines, we must also devel-op notions of commitments, protocols, and newways of modeling and specifying desirable interac-tion goals; understanding teamwork and coordinat-ed problem solving and how both can help make aservice-oriented system more robust in the face ofvarious exceptions; and monitoring the behavior oflarge-scale systems composed of heterogeneouscomponents with their own aims and objectives.

Current multiagent and service-oriented sys-tems operate according to fixed protocols, butmany application domains are uncertain and don’thave that luxury. This requires additional work onprobabilistic discourse, negotiation, and interactionprotocols (here, protocols are considered to be sta-tistical entities) — specifically, embedded instruc-tions (systems should present interaction norms tousers and provide for discoverable interaction,negotiation, and discourse protocols) and system-wide properties and emergent phenomena.

Predicting the behavior of large-scale agentsystems is extremely difficult — sometimes impos-sible — because of emergent behavior. Tools andtechniques from statistical physics could helpbecause concepts such as phase transitions, uni-versality, entropy, and convergence have analogsin MASs. Network theory is relevant as well,because of its study of the interplay between struc-ture and interaction, as is sociology (for under-standing social dynamics).

The human aspects of these technologies — suchas modeling emotion and incorporating it intosocial-system simulations — are crucial for ensur-ing that people are comfortable with an expandedrole for agents. Key trade-offs between trust andautonomy inform adjustable autonomy. Likewise,privacy is inherently tied to social norms and expec-tations, which can be expected to evolve as peoplereassess the risk/benefit trade-off between trust andautonomy.

Future research will extend MASs in scale, het-erogeneity, embodiment in the physical world,

and lifetime, and as MASs take SOC into newdimensions we’ll be able to explore, understand,and control the complex and intertwined relation-ships of the real world in unimaginable new ways.

A large community of academic and industrialresearchers is poised to conduct the research outlinedin this article, and much of it will likely be accom-

plished within five years. The most important MASand SOC applications in the near term will be infocused domains, such as the interpretation of infor-mation from sensor networks, in which the MASalready has a decision-support role. As a result, thehuman-agent-robotic interface will require a meansfor modeling, enacting, and monitoring high-levelprotocols. We’ll also need models of reputation andtrust that agents can maintain by themselves in adistributed fashion. Such models could assist withservice location and selection based on additionalconsiderations such as provenance and empiricalevaluation of information sources.

We’ll also see the emergence of a stable infra-structure for large-scale, multiagent simulation andcontrol. Agents will represent active entities (such aspeople and traffic controllers), animate the inani-mate parts of an environment (such as retail goodson a store shelf), represent individuals’ interests (suchas in e-commerce or auctions), and even model andrepresent environments. For the special case of e-government, agents representing constituents couldhelp elected officials gauge their constituents’ opin-ions and preferences, resulting in better buy-in forlegislated decisions.

Within 10 years from now, the scope of MASand SOC applications will increase in terms offunctionality (a smart house, for example, is a mul-tifunction system) or geography (such as intelligenthighway systems and alert forwarding). For suchapplications to scale to realistic size, we’ll needconcepts and techniques for teamwork that can beapplied routinely, including negotiation amongteam members, planning and replanning in the faceof environmental uncertainty, and dynamism.Adaptive, incentive-based mechanisms for produc-ing trustworthy behavior will also be crucial.

In 15 years, we can expect to see complete incor-poration of the MAS into the system it supports, pro-ducing an awareness of its system and itself thatwould enable it to detect and correct any misbehav-ior. To realize this full immersion, we’ll need tounderstand system-level properties and socialdynamics with respect to adaptive corrective behav-iors and reputation-based trust. We’ll achieve thisunderstanding and awareness via effective modelsof social systems that enable developers to adjustsimulations to align them better with the real world.In such contexts, our concern will be with ethicalagents that operate according to articulated anddesirable philosophies. Ethics will also characterizethe behavior of successful services as they interactand collaborate with each other in SOC.

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AcknowledgementsThe US National Science Foundation under grant number IIS-

0531746 and the Defense Advanced Research Projects Agency

under grant number HR0011-05-1-0020 supported the retreat

at which we formulated the ideas described in this article.

References

1. M. Singh and M. Huhns, Service-Oriented Computing:

Semantics, Processes, Agents, John Wiley & Sons, 2005.

2. W.R. Ashby, “Requisite Variety and Its Implications for the

Control of Complex Systems,” Cybernetica, vol. 1, no. 2,

1958, pp. 83–99.

3. R.C. Conant and W.R. Ashby, “Every Good Regulator of a

System Must Be a Model of that System,” Int’l J. Systems

Science, vol. 1, no. 2, 1970, pp. 89–97.

4. N.R. Jennings and M. Wooldridge, eds., Agent Technology:

Foundations, Applications and Markets, Springer Verlag,

1998.

5. H.V.D. Parunak, “Industrial and Practical Applications of

Distributed AI,” Multi-Agent Systems, G. Weiss, ed., MIT

Press, 1999, pp. 377–421.

6. V.T. Holderfield, M.N. Huhns, and R.L. Zavala, “Achieving

Software Robustness Via Large-Scale Multiagent Systems,”

Software Eng. for Large-Scale Multi-Agent Systems, LNCS

2603, Springer Verlag, 2003, pp. 199–215.

7. R.L. Zavala and M.N. Huhns, “On Building Robust Web Ser-

vice-Based Applications,” Extending Web Services Tech-

nologies: The Use of Multi-Agent Approaches, L. Cavedon

et al., eds., Kluwer Academic, 2004, pp. 293–310.

8. L. Gasser and K. Kakugawa, “MACE3J: Fast Flexible Dis-

tributed Simulation of Large, Large-Grain Multi-Agent

Systems,” Proc. 1st Int’l Conf. Autonomous Agents and

Multi Agent Systems, ACM Press, 2002, pp. 745–752.

Michael N. Huhns is the NCR professor of computer science and

engineering at the University of South Carolina. Contact

him at huhns@sc.edu.

Munindar P. Singh is a professor of computer science at North Car-

olina State University. Contact him at singh@ncsu.edu.

Mark Burstein is a division scientist and director of the Human

Centered Computing Group at BBN Technologies. Contact

him at burstein@bbn.com.

Keith Decker is an associate professor in computer and infor-

mation sciences at the University of Delaware. Contact him

at decker@cis.udel.edu.

Edmund Durfee is a professor of electrical engineering and

computer science at the University of Michigan. Contact

him at durfee@umich.edu.

Tim Finin is a professor of computer science and electrical engi-

neering at the University of Maryland Baltimore County.

Contact him at finin@umbc.edu.

Les Gasser is an associate professor at the University of Illinois.

Contact him at gasser@uiuc.edu.

Hrishikesh Goradia is a PhD candidate in computer science at

the University of South Carolina. Contact him at goradia@

engr.sc.edu.

Nick Jennings is a professor in the School of Electronics and

Computer Science at Southampton University. Contact him

at nrj@ecs.soton.ac.uk.

Kiran Lakkaraju is a PhD candidate in computer science at the

University of Illinois. Contact him at klakkara@uiuc.edu.

Hideyuki Nakashima is the president of Future University in

Hakodate, Japan. Contact him at h.nakashima@aist.go.jp.

H. Van Dyke Parunak is the chief scientist for Altarum in Ann

Arbor, Michigan. Contact him at van.parunak@altarum.org.

Jeffrey S. Rosenschein is an associate professor and Chairman

of Studies for Computer Engineering at Hebrew University.

Contact him at jeff@cs.huji.ac.il.

Alicia Ruvinsky is a PhD candidate in computer science at the

University of South Carolina. Contact her at ruvinsky@

engr.sc.edu.

Gita Sukthankar is a PhD student in robotics at Carnegie Mel-

lon University. Contact her at gitars@cs.cmu.edu.

Samarth Swarup is a PhD candidate in computer science at the

University of Illinois. Contact him at swarup@uiuc.edu.

Katia Sycara is a research professor at Carnegie Mellon Univer-

sity and holds the Sixth Century Chair in Computing Sci-

ence at the University of Aberdeen. Contact her at

katia@cs.cmu.edu.

Milind Tambe is an associate professor in computer science at

the University of Southern California. Contact him at

tambe@usc.edu.

Tom Wagner is a program manager in the IPTO office at DARPA.

Contact him at twagner@darpa.mil.

Laura Zavala is a PhD candidate in computer science at the Uni-

versity of South Carolina. Contact her at zavalagu@

engr.sc.edu.

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