Summary Report of The First International Competition on Computational Models of Argumentation

Computational models of argumentation are an active research discipline within Artificial Intelligence that has grown since the beginning of the 1990s (Dung 1995). While still a young field when compared to areas such as SAT solving and Logic Programming, the argumentation community is very active, with a conference series (COMMA, which began in 2006) and a variety of workshops and special issues of journals. Argumentation has also worked its way into a variety of applications. For example, Williams et al. (2015) described how argumentation techniques are used for recommending cancer treatments, while Toniolo et al. (2015) detail how argumentation-based techniques can support critical thinking and collaborative scientific inquiry or intelligence analysis. Many of the problems that argumentation deals with are computationally difficult, and applications utilising argumentation therefore require efficient solvers. To encourage this line of research, we organised the First International Competition on Computational Models of Argumentation (ICCMA), with the intention of assessing and promoting state of the art solvers for abstract argumentation problems, and to identify families of challenging benchmarks for such solvers. The objective of ICCMA’15 is to allow researchers to compare the performance of different solvers systematically on common benchmarks and rules. Moreover, as witnessed by competitions in other AI disciplines such as planning and SAT solving, we see ICCMA as a new pillar of the community which provides information and insights on the current state of the art, and highlights future challenges and developments. This article summarises the first ICCMA held in 2015 (ICCMA’15). In this competition, solvers were invited to address standard decision and enumeration problems of abstract argumentation frameworks (Dunne and Wooldridge 2009). Solvers’ performance is evaluated based on their time taken to provide a correct solution for a problem; incorrect results were discarded. More information about the competition, including complete results and benchmarks, can be found on the ICCMA website

An Ontological-based Knowledge-Representation Formalism for Case-Based Argumentation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-014-9524-3[EN] In open multi-agent systems, agents can enter or leave the system, interact, form societies, and have dependency relations with each other. In these systems, when agents have to collaborate or coordinate their activities to achieve their objectives, their different interests and preferences can come into conflict. Argumentation is a powerful technique to harmonise these conflicts. However, in many situations the social context of agents determines the way in which agents can argue to reach agreements. In this paper, we advance research in the computational representation of argumentation frameworks by proposing a new ontologicalbased, knowledge-representation formalism for the design of open MAS in which the participating software agents are able to manage and exchange arguments with each other taking into account the agents’ social context. This formalism is the core of a case-based argumentation framework for agent societies. In addition, we present an example of the performance of the formalism in a real domain that manages the requests received by the technicians of a call centre.This work is supported by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO II/2013/019].Heras Barberá, SM.; Botti, V.; Julian Inglada, VJ. (2014). An Ontological-based Knowledge-Representation Formalism for Case-Based Argumentation. Information Systems Frontiers. 1-20. https://doi.org/10.1007/s10796-014-9524-3S120Amgoud, L. (2005). An argumentation-based model for reasoning about coalition structures. In 2nd international workshop on argumentation in multi-agent systems, argmas-05(pp. 1–12). Springer.Amgoud, L., Dimopolous, Y., Moraitis, P. (2007). A unified and general framework for argumentation-based negotiation. In 6th international joint conference on autonomous agents and multiagent systems, AAMAS-07. IFAAMAS.Atkinson, K., & Bench-Capon, T. (2008). Abstract argumentation scheme frameworks. In Proceedings of the 13th international conference on artificial intelligence: methodology, systems and applications, AIMSA-08, lecture notes in artificial intelligence (Vol. 5253, pp. 220–234). Springer.Aulinas, M., Tolchinsky, P., Turon, C., Poch, M., Cortés, U. (2012). Argumentation-based framework for industrial wastewater discharges management. Engineering Applications of Artificial Intelligence, 25(2), 317–325.Bench-Capon, T., & Atkinson, K. (2009). Argumentation in artificial intelligence, chap. abstract argumentation and values (pp. 45–64). Springer.Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150(1-2), 97–143.Bulling, N., Dix, J., Chesñevar, C.I. (2008). Modelling coalitions: ATL + argumentation. In Proceedings of the 7th international joint conference on autonomous agents and multiagent systems, AAMAS-08 (Vol. 2, pp. 681–688). ACM Press.Chesñevar, C., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G., South, M., Vreeswijk, G., Willmott, S. (2006). Towards an argument interchange format. The Knowledge Engineering Review, 21(4), 293–316.Diaz-Agudo, B., & Gonzalez-Calero, P.A. (2007). Ontologies: A handbook of principles, concepts and applications in information systems, integrated series in information systems, chap. an ontological approach to develop knowledge intensive cbr systems (Vol. 14, pp. 173–214). Springer.Dung, P.M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and N -person games. Artificial Intelligence, 77, 321–357.Ferber, J., Gutknecht, O., Michel, F. (2004). From agents to organizations: An organizational view of multi-agent systems. In Agent-oriented software engineering VI, LNCS (Vol. 2935, pp. 214–230.) Springer-Verlag.Hadidi, N., Dimopolous, Y., Moraitis, P. (2010). Argumentative alternating offers. In 9th international conference on autonomous agents and multiagent systems, AAMAS-10 (pp. 441–448). IFAAMAS.Heras, S., Atkinson, K., Botti, V., Grasso, F., Julián, V., McBurney, P. (2010). How argumentation can enhance dialogues in social networks. In Proceedings of the 3rd international conference on computational models of argument, COMMA-10, frontiers in artificial intelligence and applications (Vol. 216, pp. 267–274). IOS Press.Heras, S., Botti, V., Julián, V. (2011). On a computational argumentation framework for agent societies. In Argumentation in multi-agent systems (pp. 123–140). Springer.Heras, S., Botti, V., Julián, V. (2012). Argument-based agreements in agent societies. Neurocomputing, 75(1), 156–162.Heras, S., Jordán, J., Botti, V., Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82–108.Jordán, J., Heras, S., Julián, V. (2011). A customer support application using argumentation in multi-agent systems. In 14th international conference on information fusion (FUSION-11) (pp. 772– 778).Karunatillake, N.C. (2006). Argumentation-based negotiation in a social context. Ph.D. thesis, School of Electronics and Computer Science, University of Southampton, UK.Karunatillake, N.C., Jennings, N.R., Rahwan, I., McBurney, P. (2009). Dialogue games that agents play within a society. Artificial Intelligence, 173(9-10), 935–981.Kraus, S., Sycara, K., Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104, 1–69.López de Mántaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M., Forbus, K., Keane, M., Watson, I. (2006). Retrieval, reuse, revision, and retention in CBR. The Knowledge Engineering Review, 20(3), 215–240.Luck, M., & McBurney, P. (2008). Computing as interaction: Agent and agreement technologies. In IEEE international conference on distributed human-machine systems. IEEE Press.Oliva, E., McBurney, P., Omicini, A. (2008). Co-argumentation artifact for agent societies. In 5th international workshop on argumentation in multi-agent systems, Argmas-08 (pp. 31–46). Springer.Ontañón, S., & Plaza, E. (2007). Learning and joint deliberation through argumentation in multi-agent systems. In 7th international conference on agents and multi-agent systems, AAMAS-07. ACM Press.Ontañón, S., & Plaza, E. (2009). Argumentation-based information exchange in prediction markets. In Argumentation in multi-agent systems, LNAI (vol. 5384, pp. 181–196). Springer.Parsons, S., Sierra, C., Jennings, N.R. (1998). Agents that reason and negotiate by arguing. Journal of Logic and Computation, 8(3), 261–292.Prakken, H. (2010). An abstract framework for argumentation with structured arguments. Argument and Computation, 1, 93–124.Prakken, H., Reed, C., Walton, D. (2005). Dialogues about the burden of proof. In Proceedings of the 10th international conference on artificial intelligence and law, ICAIL-05 (pp. 115–124). ACM Press.Sierra, C., Botti, V., Ossowski, S. (2011). Agreement computing. KI - Künstliche Intelligenz 10.1007/s13218-010-0070-y .Soh, L.K., & Tsatsoulis, C. (2005). A real-time negotiation model and a multi-agent sensor network implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215–271.Walton, D., Reed, C., Macagno, F. (2008). Argumentation schemes. Cambridge University Press.Wardeh, M., Bench-Capon, T., Coenen, F.P. (2008). PISA - pooling information from several agents: Multiplayer argumentation from experience. In Proceedings of the 28th SGAI international conference on artificial intelligence, AI-2008 (pp. 133–146). Springer.Wardeh, M., Bench-Capon, T., Coenen, F.P. (2009). PADUA: A protocol for argumentation dialogue using association rules. AI and Law, 17(3), 183–215.Wardeh, M., Coenen, F., Bench-Capon, T. (2010). Arguing in groups. In 3rd international conference on computational models of argument, COMMA-10 (pp. 475–486). IOS Press.Willmott, S., Vreeswijk, G., Chesñevar, C., South, M., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G. (2006). Towards an argument interchange format for multi-agent systems. In 3rd international workshop on argumentation in multi-agent systems, ArgMAS-06 (pp. 17–34). Springer.Wyner, A., & Schneider, J. (2012). Arguing from a point of view. In Proceedings of the first international conference on agreement technologies

4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings

This volume constitutes the refereed proceedings of the 4th International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2009, held in Salamanca, Spain, in June 2009. The 85 papers presented, were carefully reviewed and selected from 206 submissions. The topics covered are agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, real world HAIS applications and data uncertainty, hybrid artificial intelligence in bioinformatics, evolutionary multiobjective machine learning, hybrid reasoning and coordination methods on multi-agent systems, methods of classifiers fusion, knowledge extraction based on evolutionary learning, hybrid systems based on bioinspired algorithms and argumentation methods, hybrid evolutionry intelligence in financial engineering

Challenges for a CBR framework for argumentation in open MAS

[EN] Nowadays, Multi-Agent Systems (MAS) are broadening their applications to open environments, where heterogeneous agents could enter into the system, form agents’ organizations and interact. The high dynamism of open MAS gives rise to potential conflicts between agents and thus, to a need for a mechanism to reach agreements. Argumentation is a natural way of harmonizing conflicts of opinion that has been applied to many disciplines, such as Case-Based Reasoning (CBR) and MAS. Some approaches that apply CBR to manage argumentation in MAS have been proposed in the literature. These improve agents’ argumentation skills by allowing them to reason and learn from experiences. In this paper, we have reviewed these approaches and identified the current contributions of the CBR methodology in this area. As a result of this work, we have proposed several open issues that must be taken into consideration to develop a CBR framework that provides the agents of an open MAS with arguing and learning capabilities.This work was partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under TIN2006-14630-C0301 project.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2009). Challenges for a CBR framework for argumentation in open MAS. Knowledge Engineering Review. 24(4):327-352. https://doi.org/10.1017/S0269888909990178S327352244Willmott S. , Vreeswijk G. , Chesñevar C. , South M. , McGinnis J. , Modgil S. , Rahwan I. , Reed C. , Simari G. 2006. Towards an argument interchange format for multi-agent systems. In Proceedings of the AAMAS International Workshop on Argumentation in Multi-Agent Systems, ArgMAS-06, 17–34.Sycara, K. P. (1990). Persuasive argumentation in negotiation. Theory and Decision, 28(3), 203-242. doi:10.1007/bf00162699Ontañón S. , Plaza E. 2006. Arguments and counterexamples in case-based joint deliberation. In AAMAS-06 Workshop on Argumentation in Multi-Agent Systems, ArgMAS-06, 36–53.Sadri F. , Toni F. , Torroni P. 2001. Dialogues for negotiation: agent varieties and dialogue sequences. In Proceedings of the 8th International Workshop on Agent Theories, Architectures, and Languages, ATAL-01, Intelligent Agents VIII 2333, 405–421. Springer.Fox J. , Parsons S. 1998. Arguing about beliefs and actions. In Applications of Uncertainty Formalisms, Lecture Notes in Computer Science 1455, 266–302. Springer.Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2), 321-357. doi:10.1016/0004-3702(94)00041-xAulinas M. , Tolchinsky P. , Turon C. , Poch M. , Cortés U. 2007. Is my spill environmentally safe? Towards an integrated management of wastewater in a river basin using agents that can argue. In 7th International IWA Symposium on Systems Analysis and Integrated Assessment in Water Management. Washington DC, USA.Amgoud L. 2003. A formal framework for handling conflicting desires. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lecture Notes in Computer Science 2711, 552–563. Springer.Armengol E. , Plaza E. 2001. Lazy induction of descriptions for relational case-based learning. In European Conference on Machine Learning, ECML-01, 13–24.Sørmo, F., Cassens, J., & Aamodt, A. (2005). Explanation in Case-Based Reasoning–Perspectives and Goals. Artificial Intelligence Review, 24(2), 109-143. doi:10.1007/s10462-005-4607-7RAHWAN, I., RAMCHURN, S. D., JENNINGS, N. R., McBURNEY, P., PARSONS, S., & SONENBERG, L. (2003). Argumentation-based negotiation. The Knowledge Engineering Review, 18(4), 343-375. doi:10.1017/s0269888904000098Brüninghaus S. , Ashley K. D. 2001. Improving the representation of legal case texts with information extraction methods. In 7th International Conference on Artificial Intelligence and Law, ICAIL-01, 42–51.Parsons, S. (1998). Agents that reason and negotiate by arguing. Journal of Logic and Computation, 8(3), 261-292. doi:10.1093/logcom/8.3.261Atkinson, K., Bench-Capon, T., & Mcburney, P. (2005). A Dialogue Game Protocol for Multi-Agent Argument over Proposals for Action. Autonomous Agents and Multi-Agent Systems, 11(2), 153-171. doi:10.1007/s10458-005-1166-xBrüninghaus S. , Ashley K. D. 2003. Predicting the outcome of case-based legal arguments. In 9th International Conference on Artificial Intelligence and Law, ICAIL-03, 233–242.Modgil S. , Tolchinsky P. , Cortés U. 2005. Towards formalising agent argumentation over the viability of human organs for transplantation. In 4th Mexican International Conference on Artificial Intelligence, MICAI-05, 928–938.Tolchinsky P. , Atkinson K. , McBurney P. , Modgil S. , Cortés U. 2007. Agents deliberating over action proposals using the ProCLAIM model. In 5th International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS-07, 32–41.Prakken, H., & Sartor, G. (1998). Artificial Intelligence and Law, 6(2/4), 231-287. doi:10.1023/a:1008278309945Gordon T. F. , Karacapilidis N. 1997. The Zeno argumentation framework. In International Conference on Artificial Intelligence and Law, ICAIL-97, ACM Press, 10–18.Tolchinsky P. , Modgil S. , Cortés U. 2006a. Argument schemes and critical questions for heterogeneous agents to argue over the viability of a human organ. In AAAI Spring Symposium Series; Argumentation for Consumers of Healthcare, 377–384.Aleven V. , Ashley K. D. 1997. Teaching case-based argumentation through a model and examples, empirical evaluation of an intelligent learning environment. In 8th World Conference of the Artificial Intelligence in Education Society, 87–94.Rahwan, I. (2005). Guest Editorial: Argumentation in Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, 11(2), 115-125. doi:10.1007/s10458-005-3079-0RISSLAND, E. L., ASHLEY, K. D., & BRANTING, L. K. (2005). Case-based reasoning and law. The Knowledge Engineering Review, 20(3), 293-298. doi:10.1017/s0269888906000701Tolchinsky, P., Cortes, U., Modgil, S., Caballero, F., & Lopez-Navidad, A. (2006). Increasing Human-Organ Transplant Availability: Argumentation-Based Agent Deliberation. IEEE Intelligent Systems, 21(6), 30-37. doi:10.1109/mis.2006.116McBurney, P., Hitchcock, D., & Parsons, S. (2006). The eightfold way of deliberation dialogue. International Journal of Intelligent Systems, 22(1), 95-132. doi:10.1002/int.20191Rissland, E. L., Ashley, K. D., & Loui, R. P. (2003). AI and Law: A fruitful synergy. Artificial Intelligence, 150(1-2), 1-15. doi:10.1016/s0004-3702(03)00122-xSoh, L.-K., & Tsatsoulis, C. (2005). A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215-271. doi:10.1007/s10458-005-0539-5Capobianco, M., Chesñevar, C. I., & Simari, G. R. (2005). Argumentation and the Dynamics of Warranted Beliefs in Changing Environments. Autonomous Agents and Multi-Agent Systems, 11(2), 127-151. doi:10.1007/s10458-005-1354-8Tolchinsky P. , Modgil S. , Cortés U. , Sànchez-Marrè M. 2006b. CBR and argument schemes for collaborative decision making. In Conference on Computational Models of Argument, COMMA-06, 144, 71–82. IOS Press.Ossowski S. , Julian V. , Bajo J. , Billhardt H. , Botti V. , Corchado J. M. 2007. Open issues in open MAS: an abstract architecture proposal. In Conferencia de la Asociacion Española para la Inteligencia Artificial, CAEPIA-07, 2, 151–160.Karacapilidis, N., & Papadias, D. (2001). Computer supported argumentation and collaborative decision making: the HERMES system. Information Systems, 26(4), 259-277. doi:10.1016/s0306-4379(01)00020-5Aamodt A. 2004. Knowledge-intensive case-based reasoning in Creek. In 7th European Conference on Case-Based Reasoning ECCBR-04, 1–15.Jakobovits H. , Vermeir D. 1999. Dialectic semantics for argumentation frameworks. In Proceedings of the 7th International Conference on Artificial Intelligence and Law, ICAIL-99, ACM Press, 53–62.Díaz-Agudo, B., & González-Calero, P. A. (s. f.). An Ontological Approach to Develop Knowledge Intensive CBR Systems. Ontologies, 173-213. doi:10.1007/978-0-387-37022-4_7Reed C. , Walton D. 2005. Towards a formal and implemented model of argumentation schemes in agent communication. In Proceedings of the 1st International Workshop in Multi-Agent Systems, ArgMAS-04, 173–188.Sycara K. 1989. Argumentation: planning other agents’ plans. In 11th International Joint Conference on Artificial Intelligence, 1, 517–523. Morgan Kaufmann Publishers, Inc.Bench-Capon, T. J. M., & Dunne, P. E. (2007). Argumentation in artificial intelligence. Artificial Intelligence, 171(10-15), 619-641. doi:10.1016/j.artint.2007.05.001Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence, 13(1-2), 81-132. doi:10.1016/0004-3702(80)90014-4Amgoud L. , Kaci S. 2004. On the generation of bipolar goals in argumentation-based negotiation. In 1st International Workshop on Argumentation in Multi-Agent Systems, ArgMAS, Lecture Notes in Computer Science 3366, 192–207. Springer.CHESÑEVAR, C., MCGINNIS, MODGIL, S., RAHWAN, I., REED, C., SIMARI, G., … WILLMOTT, S. (2006). Towards an argument interchange format. The Knowledge Engineering Review, 21(4), 293-316. doi:10.1017/s0269888906001044Rahwan I. , Amgoud L. 2006. An argumentation-based approach for practical reasoning. In Proceedings of the 5th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS-06, ACM Press, 347–354.Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155-169. doi:10.1007/bf01405730Soh L.-K. , Tsatsoulis C. 2001b. Reflective negotiating agents for real-time multisensor target tracking. In International Joint Conference on Artificial Intelligence, IJCAI-01, 1121–1127.Eemeren, F. H. van, & Grootendorst, R. (1984). Speech Acts in Argumentative Discussions. doi:10.1515/9783110846089Rissland E. L. , Skalak D. B. , Friedman M. T. 1993. Bankxx: a program to generate argument through case-based search. In International Conference on Artificial Intelligence and Law, ICAIL-93, 117–124.Sycara K. 1987. Resolving Adversarial Conflicts: An Approach Integrating Case-Based and Analytic Methods, PhD thesis, School of Information and Computer Science. Georgia Institute of Technology.Ontañón S. , Plaza E. 2007. Learning and joint deliberation through argumentation in multi-agent systems. In International Conference on Autonomous Agents and Multiagent Systems, AAMAS-07, 971–978.Rissland, E. L., & Skalak, D. B. (1991). CABARET: rule interpretation in a hybrid architecture. International Journal of Man-Machine Studies, 34(6), 839-887. doi:10.1016/0020-7373(91)90013-wDaniels J. J. , Rissland E. L. 1997. Finding legally relevant passages in case opinions. In 6th International Conference on Artificial Intelligence and Law, ICAIL-97, 39–47.Brüninghaus S. , Ashley K. D. 2005. Generating legal arguments and predictions from case texts. In 10th International Conference on Artificial Intelligence and Law, ICAIL-05, 65–74.Simari G. R. , García A. J. , Capobianco M. 2004. Actions, planning and defeasible reasoning. In Proceedings of the 10th International Workshop on Non-monotonic Reasoning, NMR-04, 377–384.Soh L.-K. , Tsatsoulis C. 2001a. Agent-based argumentative negotiations with case-based reasoning. In AAAI Fall Symposium on Negotiation Methods for Autonomous Cooperative Systems, 16–25.Ashley, K. D. (1991). Reasoning with cases and hypotheticals in HYPO. International Journal of Man-Machine Studies, 34(6), 753-796. doi:10.1016/0020-7373(91)90011-uHulstijn J. , van der Torre L. 2004, Combining goal generation and planning in an argumentation framework. In Proceedings of the Workshop on Argument, Dialogue and Decision. International Workshop on Non-monotonic Reasoning, NMR-04, 212–218.Karacapilidis N. , Trousse B. , Papadias D. 1997. Using case-based reasoning for argumentation with multiple viewpoints. In 2nd International Conference on Case-Based Reasoning, ICCBR-97, 541–552.Branting, L. K. (1991). Building explanations from rules and structured cases. International Journal of Man-Machine Studies, 34(6), 797-837. doi:10.1016/0020-7373(91)90012-

Research opportunities for argumentation in social networks

Nowadays, many websites allow social networking between their users in an explicit or implicit way. In this work, we show how argumentation schemes theory can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that held social networks between their users. Two real case studies are studied and analysed. Then, guidelines to enhance social decision support and recommendations with argumentation are provided.This work summarises results of the authors joint research, funded by an STMS of the Agreement Technologies COST Action 0801, by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO 2008/051].Heras Barberá, SM.; Atkinson, KM.; Botti Navarro, VJ.; Grasso, F.; Julian Inglada, VJ.; Mcburney, PJ. (2013). Research opportunities for argumentation in social networks. Artificial Intelligence Review. 39(1):39-62. doi:10.1007/s10462-012-9389-0S3962391Amgoud L (2009) Argumentation for decision making. Argumentation in artificial intelligence. Springer, BerlinAnderson P (2007) What is Web 2.0? Ideas, technologies and implications for education. JISC Iechnology and Standards Watch reportBentahar J, Meyer CJJ, Moulin B (2007) Securing agent-oriented systems: an argumentation and reputation-based approach. In: Proceedings of the 4th international conference on information technology: new generations (ITNG 2007), IEEE Computer Society, pp 507–515Buckingham Shum S (2008) Cohere: towards Web 2.0 argumentation. In: Proceedings of the 2nd international conference on computational models of argument, COMMA, pp 28–30Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12:331–370Cartwright D, Atkinson K (2008) Political engagement through tools for argumentation. In: Proceedings of the second international conference on computational models of argument (COMMA 2008), pp 116–127Chesñevar C, McGinnis J, Modgil S, Rahwan I, Reed C, Simari G, South M, Vreeswijk G, Willmott S (2006) Towards an argument interchange format. Knowl Eng Rev 21(4):293–316Chesñevar CI, Maguitman AG, Gonzàlez MP (2009) Empowering recommendation technologies through argumentation. Argumentation in artificial intelligence. Springer, Berlin, pp 403–422García AJ, Dix J, Simari GR (2009) Argument-based logic programming. Argumentation in artificial intelligence. Springer, BerlinGolbeck J (2006) Generating predictive movie recommendations from trust in social networks. In: Proceedings of the fourth international conference on trust management, LNCS, vol 3986, 93–104Gordon T, Prakken H, Walton D (2007) The Carneades model of argument and burden of proof. Artif Intell 171(10–15):875–896Guha R, Kumar R, Raghavan P, Tomkins A (2004) Propagating trust and distrust. In: Proceedings of the 13th international conference on, World Wide Web, pp 403–412Heras S, Navarro M, Botti V, Julián V (2009) Applying dialogue games to manage recommendation in social networks. In: Proceedings of the 6th international workshop on argumentation in multi-agent aystems, ArgMASHeras S, Atkinson K, Botti V, Grasso F, Julián V, McBurney P (2010a) How argumentation can enhance dialogues in social networks. In: Proceedings of the 3rd international conference on computational models of argument, COMMA, vol 216, pp 267–274Heras S, Atkinson K, Botti V, Grasso F, Julián V, McBurney P (2010b) Applying argumentation to enhance dialogues in social networks. In: ECAI 2010 workshop on computational models of natural argument, CMNA, pp 10–17Karacapilidis N, Tzagarakis M (2007) Web-based collaboration and decision making support: a multi-disciplinary approach. Web-Based Learn Teach Technol 2(4):12–23Kim D, Benbasat I (2003) Trust-related arguments in internet stores: a framework for evaluation. J Electron Commer Res 4(2):49–64Kim D, Benbasat I (2006) The effects of trust-assuring arguments on consumer trust in internet stores: application of Toulmin’s model of argumentation. Inf Syst Rese 17(3):286–300Laera L, Tamma V, Euzenat J, Bench-Capon T, Payne T (2006) Reaching agreement over ontology alignments. In: Proceedings of the 5th international semantic web conference (ISWC 2006)Lange C, Bojãrs U, Groza T, Breslin J, Handschuh S (2008) Expressing argumentative discussions in social media sites. In: Social data on the web (SDoW2008) workshop at the 7th international semantic web conferenceLinden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80Linden G, Hong J, Stonebraker M, Guzdial M (2009) Recommendation algorithms, online privacy and more. Commun ACM, 52(5)Mika P (2007) Ontologies are us: a unified model of social networks and semantics. J Web Semant 5(1):5–15Montaner M, López B, de la Rosa JL (2002) Opinion-based filtering through trust. In: Cooperative information agents VI, LNCS, vol 2446, pp 127–144Ontañón S, Plaza E (2008) Argumentation-based information exchange in prediction markets. In: Proceedings of the 5th international workshop on argumentation in multi-agent systems, ArgMASPazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web, LNCS, vol 4321, pp 325–341Rahwan I, Zablith F, Reed C (2007) Laying the foundations for a world wide argument web. Artif Intell 171(10–15):897–921Rahwan I, Banihashemi B (2008) Arguments in OWL: a progress report. In: Proceedings of the 2nd international conference on computational models of argument (COMMA), pp 297–310Reed C, Walton D (2007) Argumentation schemes in dialogue. In: Dissensus and the search for common ground, OSSA-07, volume CD-ROM, pp 1–11Sabater J, Sierra C (2002) Reputation and social network analysis in multi-agent systems. In: Proceedings of the 1st international joint conference on autonomous agents and multiagent systems, vol 1, pp 475–482Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5:115–153Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive web, LNCS, vol 4321, pp 291–324Schneider J, Groza T, Passant A (2012) A review of argumentation for the aocial semantic web. Semantic web-interoperability, usability, applicability. IOS Press, Washington, DCTempich C, Pinto HS, Sure Y, Staab S (2005) An argumentation ontology for distributed, loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT). In: Proceedings of the 2nd European semantic web conference, ESWC, pp 241–256Toulmin SE (1958) The uses of argument. Cambridge University Press, Cambridge, UKTrojahn C, Quaresma P, Vieira R, Isaac A (2009) Comparing argumentation frameworks for composite ontology matching. in: Proceedings of the 6th international workshop on argumentation in multi-agent systems, ArgMASTruthMapping. http://truthmapping.com/Walter FE, Battiston S, Schweitzer F (2007) A model of a trust-based recommendation system on a social network. J Auton Agents Multi-Agent Syst 16(1):57–74Walton D, Krabbe E (1995) Commitment in dialogue: basic concepts of interpersonal reasoning. State University of New York Press, New York, NYWalton D, Reed C, Macagno F (2008) Argumentation schemes. Cambridge University Press, CambridgeWells S, Gourlay C, Reed C (2009) Argument blogging. Computational models of natural argument, CMNAWyner A, Schneider J (2012) Arguing from a point of view. In: Proceedings of the first international conference on agreement technologie

Properties of ABA+ for Non-Monotonic Reasoning

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