NOVEMBER/DECEMBER 2007 1541-1672/07/$25.00 © 2007 IEEE 21Published by the IEEE Computer Society

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ArgumentationTechnologyIyad Rahwan, University of Edinburgh and British University in Dubai

Peter McBurney, University of Liverpool

W ith the Internet’s rise to prominence, we’re moving to a new understanding of

the nature of computation.1 From the times of Leibniz and Babbage until the

late 1950s, computation was understood as calculation, or the manipulation of numbers.

Throughout the next decade (and still perhaps for many people), computation came to be

understood as information processing, or the manipu-lation of data. With the rise of AI, logic programming,and expert systems, the idea arose of computation ascognition, or the manipulation of concepts. With thegrowth of the Internet and the World Wide Web, a newmetaphor is appropriate: computation as interaction,or the joint manipulation of concepts and actions bydiscrete entities, both human and software agents.

To the extent that such agents are autonomous, noone agent can impose its will on another. To the extentthat agents are intelligent, they will need to persuadeone another to adopt particular beliefs or courses ofaction, or negotiate with one another to divide scarceresources between them. Such activities are exam-ples of argument, which we might define as rational,or reason-based, interaction between autonomous andintelligent agents to achieve particular goals. Argu-mentation, the study of argument, goes back a longtime. Aristotle, for example, wrote on the topic around350 BCE,2 starting a scholarly discourse that contin-ued, with the help of Islamic and Roman Catholicphilosophers, through the Middle Ages down to mod-ern times. The study of argument by Indian philoso-phers has a similarly long history.

Applications of computerargumentation

Applications of argumentation technologies havedeveloped over the last two decades. The first appli-cations were primarily to expert systems and tutorialsystems, explaining their recommendations or deci-sions.3 Because of medical applications’prominencein early expert systems, it was perhaps natural that themain center for the initial development of argumen-tation technologies was the Advanced ComputationLaboratory at the Imperial Cancer Research Fund

(now called Cancer Research UK). The lab, foundedand led by John Fox, was part of the the largest can-cer research organization in Europe. Applicationsdeveloped there included systems to advise doctorson patient-specific medications, including the argu-ments for and against each proposed medication, andsystems to advise doctors and patients on the diag-nostic testing and treatment of breast cancers. In thisspecial issue, “Argumentation-Based Inference andDecision Making—A Medical Perspective,” by JohnFox, David Glasspool, Dan Grecu, Sanjay Modgil,Matthew South, and Vivek Patkar, reviews these andother medical applications.

It was a short step from these applications to sys-tems undertaking automated argument generationconcerning, for example, the toxicity properties ofnew chemicals or the possible health and safety risksof some new venture. In the absence of complete oraccurate information, argumentation is a means toidentify and organize what can be justifiably con-cluded, and to present it, systematically, to humanusers or to merge it with the justified conclusions ofother machines. The DARPA-funded Genoa projectwas a similar attempt to handle incomplete and incon-sistent information from multiple sources using argu-mentation to analyze all the information relevant todecision making during a foreign geopolitical crisis.4

These first-generation applications typically reliedon relatively simple argumentation theories, whichisn’t surprising given that the formal theory of argu-mentation is still in its infancy. For the current stateof argumentation theory, see the recent reviews byCarlos Chesñevar,Ana Maguitman, and Ronald Loui;5

Henry Prakken and Gerard Vreeswijk;6 and TrevorBench-Capon and Paul Dunne7 (the third review intro-duces a special issue of Artificial Intelligence on

computational argumentation). The applica-tion of argumentation theories to real-worldapplication domains involves a range of tech-nologies. Such technologies include those forsupporting the representation, elicitation, stor-age, manipulation, and presentation of argu-ments. In addition, we need technologies andframeworks for generating and consideringarguments, engaging in argumentative inter-actions (with other machines or with hu-mans), mediating between arguments, andresolving them.

In this issueThis special issue of IEEE Intelligent Sys-

tems contains articles covering many aspectsof these technologies, often focusing on par-ticular application domains.

Intrinsically, the argumentation process takesinto account conflicting information. Whenmultiple arguments for and against a particularclaim are presented, it’s important to reconcilethese conflicts and calculate whether a givenclaim is acceptable. In “Computing Argumentsand Attacks in Assumption-Based Argumenta-tion,” Dorian Gaertner and Francesca Toni pre-sent an algorithm for doing exactly this.

Two articles describe argumentation thatsupports medical tasks. The first is the articleby John Fox and his colleagues that we men-tioned earlier. They view argumentation as ameans for inspecting and manipulating evi-dence and for supporting decision making. In“Portia: A User-Adapted Persuasion Systemin the Healthy-Eating Domain,” Irene Maz-zotta, Fiorella de Rosis, and Valeria Carofigliopresent a system that uses emotional strate-gies to persuade users to adopt healthy eatinghabits. They elicited these strategies by ana-lyzing a corpus of natural language messages,which highlighted that human persuadersemploy emotional strategies far more fre-quently than purely rational ones. Emotionalargumentation has received little attention inthe literature, but an understanding of emo-tions in argument will be crucial for buildingsystems that argue with humans.

Knowledge-based systems today oftenrequire a collaborative effort to engineer for-mal ontologies describing a domain. A cen-tral problem in collaborative ontology engi-neering is that views on how to best describea domain often conflict. In “Argumentation-Based Ontology Engineering,” ChristophTempich, Elena Simperl, Markus Luczak,Rudi Studer, and H. Sofia Pinto present aframework and tools for supporting agreementin ontology-engineering discussions. They

also report on case studies using these tools.Research has been increasing on the

semantic annotation of natural languagearguments (for example, the Araucaria8 andArgDF9 systems). This raises the question ofhow to structure user interaction with thiskind of content, especially where differentauthors represent multiple annotated argu-ments. In “Dialogical Argument as an Inter-face to Complex Debates,” Chris Reed andSimon Wells offer an approach in which theuser mediates a virtual discussion betweensoftware agents representing different view-points. The discussion takes place through adialogue game protocol, which also lets theuser express his or her opinion while inter-acting with the system, thus facilitating fur-ther knowledge elicitation.

Argumentation also provides a rich, intu-itive metaphor for interaction among distrib-uted autonomous or semiautonomous entities,such as software agents and Web services. In“Argumentation in the Semantic Web,” PaoloTorroni, Marco Gavanelli, and Federico Che-sani present the ArgSCIFF architecture, whichprovides a framework for exploiting argu-mentation as a means for flexible interactionbetween Web services. The framework relieson an argumentation machinery based on theSCIFF (iff with constraints for agent societies)Abductive-Logic-Programming framework.In “An Argumentation Framework for Com-munities of Web Services,” Jamal Bentahar,Zakaria Maamar, Djamal Benslimane, andPhilippe Thiran present a framework, based onHorn theory, for argumentation in Web ser-vices composition. They aim to enable Webservices to persuade one another to join a com-munity of Web services. The services negoti-ate the terms of service composition in peer-to-peer fashion.

In “Argumentation-Based Agent Interac-tion in an Ambient-Intelligence Context,”Pavlos Moraitis and Nikolaos Spanoudakisfurther highlight argumentation’s significantpotential as a means for interaction among ser-vices. Their framework addresses conflictingviews related to dealing with a user’s physi-cal impairments, in an ambient-intelligencecontext. When a user suffers from a combi-nation of impairments, various assistantagents engage in argument-based interactionto agree on the user’s needs.

ChallengesBench-Capon and Dunne’s review paper7

provides an excellent outline of the key chal-lenges facing argumentation theory in com-

puter science and AI. In addition to the the-oretical challenges they list, significantpractical challenges to greater adoption ofargumentation methods and systems alsoexist, some of which will require prior orsimultaneous theoretical development.

We see six key practical challenges. First,frameworks and tools for diagrammatic rep-resentation of arguments and for automatedreasoning over such representations are stillin their infancy. Although published researchon argument diagramming dates from RichardWhately’s representation of 1836, John HenryWigmore’s legal charts of 1917, and StephenToulmin’s influential model of argument of1958, only recently have computer scientistsexplored this issue. As demonstrated in puremathematics by Euclidean geometry and bycategory theory, human graphical reasoningover diagrams can involve sophisticated for-mal inference. Such reasoning over argumen-tation diagrams will need to be automated forthe successful deployment of large-scale argu-mentation systems. Chris Reed, Douglas Wal-ton, and Fabrizio Macagno provide a recentreview of work in this area.10

Second, proven software engineeringframeworks, methods, and tools specificallyfor designing and creating argumentationapplications don’t yet exist. While some com-bination of standard agent-oriented softwareengineering (AOSE) methods and knowledge-elicitation and knowledge-engineering meth-ods might prove appropriate for the engineer-ing of argumentation systems, this is by nomeans obvious a priori. To our knowledge, thisarea has seen no research. As with the devel-opment of AOSE methods and tools (whichare themselves still in early development), thiswould require a mix of theoretical and practi-cal work, with each aspect informing the other.

The development of the Argument Inter-change Format (AIF),11 an initial standard forthe exchange of arguments between machines,is a major step toward automated exchangeof arguments between intelligent softwareagents. Following from this work, a third chal-lenge is to develop a sophisticated under-standing of the properties of different agentinteraction protocols and communication lan-guages under different circumstances, and agood sense of when to use which protocol orlanguage. This challenge will require consid-erable work—both theoretical and applied—to answer questions such as, what protocolshould two agents use to undertake a negoti-ation, for example, and why? Should a nego-tiation always require the same protocol, or

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22 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS

should the protocol depend on the type ofnegotiation, the number of participants, thenature of the resource or task being allocated,the time allowed, and so on? Associated withthe properties of protocols and languages isthe need to understand the strategies and tac-tics appropriate for participants under suchprotocols. Part of this understanding will arisefrom a better semantic understanding of thenature of argumentative interactions betweenmultiple autonomous agents.

Fourth, as with knowledge engineering ingeneral, most real-world application domainsinvolve a surfeit of arguments and hence anargumentation-engineering bottleneck in thecomputational representation of arguments.Solutions to this bottleneck are required forargumentation systems’wide deployment andadoption. Perhaps the use of user-generatedcontent and content annotation—such as folks-onomies in semantic classifications—willhelp solve this challenge. The development oftheoretical and software components thatmight enable this has commenced,9 buildingon the AIF and exploiting Semantic Web tech-nologies. The development of domain-specificframeworks, argument inference, and tools12

will likely assist with this challenge.Several of these challenges are part of a

larger scalability challenge, which is fifth inour list. For argumentation systems to support,for example, millions of people engaged indeliberation about some matter of public pol-icy, considerable work is needed to ensure thatapplications can scale. Prediction markets pro-vide a means to organize the quantitativeviews of large numbers of people on someissue, such as the likelihood of increased inter-est rates or of an influenza epidemic. Wedesire similar systems that organize people’squalitative arguments and the justificationsthey hold for their views.

The sixth challenge is that the links betweenargumentation and other disciplines need at-tention for argumentation systems to find apermanent place in the ecosystem of intelli-gent computer systems. Examples include therelationships between argumentation and

• quantitative formalisms for representinguncertainty, such as probability theory andDempster-Shafer theory;

• game theory, in systems with multiple,competing participants;

• political theory—for example, in systemssupporting deliberative democracy; and

• organization theory—for example, in sys-tems supporting collaborative work.

In addition to much else, Aristotle was thefounder of logic, which is the study of rep-

resentations of certain kinds of arguments. Ifhe and his colleagues had had to apply forresearch grant funding, they wouldn’t havebeen able to point to early commercial spin-offs from their research. But spin-offs haveeventually arrived. The development of themodern computer has been greatly influencedby developments in formal logic, and viceversa.13 But if logic is the means by whichcomputers think, then argumentation is themeans by which intelligent computers inter-act, both with one another and with humans.We foresee a bright future for argumentationand argumentation technologies.

AcknowledgmentsWe’re grateful for financial support from the

European Commission’s Information SocietyTechnologies (IST) program, through ProjectASPIC: Argumentation Service Platform with Inte-grated Components (IST-FP6-002307). We alsothank the editor in chief and his staff for letting usorganize this special issue and for their support andencouragement throughout.

References

1. M. Luck et al., Agent Technology: Comput-ing as Interaction. A Roadmap for AgentBased Computing, AgentLink III, 2005;www.agentlink.org/roadmap/al3rm.pdf.

2. Aristotle, Topics, Clarendon Press, 1928.

3. D.V. Carbogim, D.S. Robertson, and J.R. Lee,“Argument-Based Applications to KnowledgeEngineering,” Knowledge Eng. Rev., vol. 15,no. 2, 2000, pp. 119–149.

4. G.I. Seffers, “Crisis System for President:DARPA’s Project Genoa Would Speed USDecision-Making,” Defense News, 30 Mar.1998, p. 1.

5. C.I. Chesñevar,A.G. Maguitman, and R.P. Loui,“Logical Models of Argument,” ACM Comput-ing Surveys, vol. 32, no. 4, 2000, pp. 337–383.

6. H. Prakken and G. Vreeswijk, “Logical Sys-tems for Defeasible Argumentation,” Hand-book of Philosophical Logic, 2nd ed., vol. 4,D.M. Gabbay and F. Guenther, eds., KluwerAcademic, 2002, pp. 219–318.

7. T.J.M. Bench-Capon and P.E. Dunne, “Argu-mentation in Artificial Intelligence,” ArtificialIntelligence, vol. 171, nos. 10–15, 2007, pp.619–641.

8. C. Reed and G. Rowe, “Araucaria: Softwarefor Argument Analysis,” Int’l J. AI Tools, vol.14, nos. 3–4, 2004, pp. 961–980.

9. I. Rahwan, F. Zablith, and C. Reed, “Layingthe Foundations for a World Wide ArgumentWeb,” Artificial Intelligence, vol. 171, nos.10–15, 2007, pp. 897–921.

10. C. Reed, D. Walton, and F. Macagno, “Argu-ment Diagramming in Logic, Law and Artifi-cial Intelligence,” Knowledge Eng. Rev., vol.22, no. 1, 2007, pp. 87–109.

11. C. Chesñevar et al., “Towards an ArgumentInterchange Format,” Knowledge Eng. Rev.,vol. 21, no. 4, 2006, pp. 293–316.

12. P. Tolchinsky et al., “Agents Deliberating overAction Proposals Using the ProCLAIM Model,”Multi-Agent Systems and Applications V,LNCS 4696, Springer, 2007, pp. 32–41.

13. J.Y. Halpern et al., “On the Unusual Effective-ness of Logic in Computer Science,” Bull. Sym-bolic Logic, vol. 7, no. 2, 2001, pp. 213–236.

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T h e A u t h o r sIyad Rahwan is a fellow in the University of Edinburgh’s School of Infor-matics and a lecturer in the Faculty of Informatics at the British Universityin Dubai. His research interests are computational models of argument andmultiagent systems. He received his PhD in information systems from theUniversity of Melbourne. He’s a member of the ACM and AAAI. Contact himat the Faculty of Informatics, British Univ. in Dubai, PO Box 502216, Dubai,UAE; irahwhan@acm.org.

Peter McBurney is a senior lecturer in the University of Liverpool’s Depart-ment of Computer Science. His research interests are multiagent systems, argu-mentation, and automated decision making. He received his PhD in computerscience from the University of Liverpool. He’s co-editor in chief of the Knowl-edge Engineering Review. Contact him at the Dept. of Computer Science,Univ. of Liverpool, Ashton Bldg., Liverpool L69 3BX, UK; mcburney@liverpool.ac.uk.