Algorithms and Complexity Results for Persuasive Argumentation

The study of arguments as abstract entities and their interaction as introduced by Dung (Artificial Intelligence 177, 1995) has become one of the most active research branches within Artificial Intelligence and Reasoning. A main issue for abstract argumentation systems is the selection of acceptable sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J. Logic Comput. 13, 2003), extends Dung's framework. It takes into account the relative strength of arguments with respect to some ranking representing an audience: an argument is subjectively accepted if it is accepted with respect to some audience, it is objectively accepted if it is accepted with respect to all audiences. Deciding whether an argument is subjectively or objectively accepted, respectively, are computationally intractable problems. In fact, the problems remain intractable under structural restrictions that render the main computational problems for non-value-based argumentation systems tractable. In this paper we identify nontrivial classes of value-based argumentation systems for which the acceptance problems are polynomial-time tractable. The classes are defined by means of structural restrictions in terms of the underlying graphical structure of the value-based system. Furthermore we show that the acceptance problems are intractable for two classes of value-based systems that where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007)

On computing explanations in argumentation

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Argumentation can be viewed as a process of generating explanations. However, existing argumentation semantics are developed for identifying acceptable arguments within a set, rather than giving concrete justifications for them. In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation. We identify different types of explanations defined in terms of the new semantics. We also give a correct computational counterpart for explanations using dispute forests

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. 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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

Arg-tuProlog: A tuProlog-based argumentation framework

Over the last decades, argumentation has become increasingly central as a frontier research within artificial intelligence (AI), especially around the notions of interpretability and explainability, which are more and more required within AI applications. In this paper we present the first prototype of Arg-tuProlog, a logic-based argumentation tool built on top of the tuProlog system. In particular, Arg-tuProlog enables defeasible reasoning and argumentation, and deals with priorities over rules. It also includes a formal method for dealing with burden of proof (burden of persuasion). Being lightweight and compliant to the requirements for micro-intelligence, Arg-tuProlog is perfectly suited for injecting argumentation into distributed pervasive systems

Decision Making Intelligent Agent on SOX Compliance over the Imports Process

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Imports Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Imports Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence