Case-Based Argumentation in Agent Societies

Hoy en día los sistemas informáticos complejos se pueden ven en términos de los servicios que ofrecen y las entidades que interactúan para proporcionar o consumir dichos servicios. Los sistemas multi-agente abiertos, donde los agentes pueden entrar o salir del sistema, interactuar y formar grupos (coaliciones de agentes u organizaciones) de forma dinámica para resolver problemas, han sido propuestos como una tecnología adecuada para implementar este nuevo paradigma informático. Sin embargo, el amplio dinamismo de estos sistemas requiere que los agentes tengan una forma de armonizar los conflictos que surgen cuando tienen que colaborar y coordinar sus actividades. En estas situaciones, los agentes necesitan un mecanismo para argumentar de forma eficiente (persuadir a otros agentes para que acepten sus puntos de vista, negociar los términos de un contrato, etc.) y poder llegar a acuerdos. La argumentación es un medio natural y efectivo para abordar los conflictos y contradicciones del conocimiento. Participando en diálogos argumentativos, los agentes pueden llegar a acuerdos con otros agentes. En un sistema multi-agente abierto, los agentes pueden formar sociedades que los vinculan a través de relaciones de dependencia. Estas relaciones pueden surgir de sus interacciones o estar predefinidas por el sistema. Además, los agentes pueden tener un conjunto de valores individuales o sociales, heredados de los grupos a los que pertenecen, que quieren promocionar. Las dependencias entre los agentes y los grupos a los que pertenecen y los valores individuales y sociales definen el contexto social del agente. Este contexto tiene una influencia decisiva en la forma en que un agente puede argumentar y llegar a acuerdos con otros agentes. Por tanto, el contexto social de los agentes debería tener una influencia decisiva en la representación computacional de sus argumentos y en el proceso de gestión de argumentos.Heras Barberá, SM. (2011). Case-Based Argumentation in Agent Societies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12497Palanci

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

Case-Based Argumentation Framework. Reasoning Process

The capability of reaching agreements is a necessary feature that large computer systems where agents interoperate must include. In these systems, agents represent self-motivated entities that have a social context, including dependency relations among them, and different preferences and beliefs. Without agreement there is no cooperation and thus, complex tasks which require the interaction of agents with different points of view cannot be performed. In this work, we follow a case-based argumentation approach for the design and implementation of Multi-Agent Systems where agents reach agreements by arguing and improve their argumentation skills from experience. A set of knowledge resources and a reasoning process that agents can use to manage their positions and arguments are presented.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2011). Case-Based Argumentation Framework. Reasoning Process. http://hdl.handle.net/10251/1109

Case-Based Argumentation Framework. Dialogue Protocol

On top of the simpler ability to interact, open MAS must include mechanisms for their agents to reach agreements by taking into account their social context. Argumentation provides MAS with a framework that assures a rational communication, which allows agents to reach agreements when conflicts of opinion arise. In this report we present the communication protocol that agents of a case-based argumentation framework use to interact when they engage in argumentation dialogues. The syntax and semantics of the framework are formalised and discussed.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2011). Case-Based Argumentation Framework. Dialogue Protocol. http://hdl.handle.net/10251/1109

Modelling dialogues in agent societies

Besides the simpler ability to interact, open multi-agent systems must include mechanisms for their agents to reach agreements by taking into account their social context. Argumentation provides multi-agent systems with a framework that assures a rational communication, which allows agents to reach agreements when conflicts of opinion arise. In this paper, we present the dialogue protocol that agents of a case-based argumentation framework can use to interact when they engage in argumentation dialogues. The syntax and semantics of the argumentation protocol are formalised and discussed. To illustrate our proposal, we have applied the protocol in the context of a water market. By using our dialogue protocol, agents represent water users that are able to explore different water allocations and justify their views about what is the best water distribution in a certain environment.This work is supported by the Spanish government Grants CONSOLIDER INGENIO 2010 CSD2007-00022, MINECO/FEDER TIN2012-36586-C03-01, and MICINN TIN2011-27652-C03-01.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2014). Modelling dialogues in agent societies. Engineering Applications of Artificial Intelligence. 34:208-226. https://doi.org/10.1016/j.engappai.2014.06.003S2082263

A Computational Argumentation Framework for Agent Societies

Starting from the idea that the social context of agents determines the way in which agents can argue and reach agreements, this context should have a decisive influence in the computational representation of arguments. In this report, we advance research in the area of computational frameworks for agent argumentation by proposing a new argumentation framework (AF) for the design of open MAS in which the participating software agents are able to manage and exchange arguments between themselves taking into account the agents¿ social context. In order to do this, we have analysed the necessary requirements for this type of framework 1 and taken into account them in the design of our framework. Also, the knowledge resources that the agents can use to manage arguments in this framework are presented in this work. In addition, if heterogeneous agents can interact in the framework, they need a common language to represent arguments and argumentation processes. To cope with this, we have also designed an argumentation ontology to represent arguments and argumentation concepts in our framework.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2011). A Computational Argumentation Framework for Agent Societies. http://hdl.handle.net/10251/1103

Case-Based Argumentation Framework. Strategies

In agent societies, agents perform complex tasks that require different levels of intelligence and give rise to interactions among them. From these interactions, conflicts of opinion can arise, specially when MAS become adaptive and open with heterogeneous agents dynamically entering in or leaving the system. Therefore, software agents willing to participate in this type of systems will require to include extra capabilities to explicitly represent and generate agreements on top of the simpler ability to interact. In addition, agents can take advantage of previous argumentation experiences to follow dialogue strategies and easily persuade other agents to accept their opinions. Our insight is that CBR can be very useful to manage argumentation in open MAS and devise argumentation strategies based on previous argumentation experiences. To demonstrate the foundations of this suggestion, this report presents the work that we have done to develop case-based argumentation strategies in agent societies. Thus, we propose a case-based argumentation framework for agent societies and define heuristic dialogue strategies based on it. The framework has been implemented and evaluated in a real customer support application.Heras Barberá, SM.; Botti Navarro, VJ.; Julian Inglada, VJ. (2011). Case-Based Argumentation Framework. Strategies. http://hdl.handle.net/10251/1109

Applying CBR to manage argumentation in MAS

[EN] The application of argumentation theories and techniques in multi-agent systems has become a prolific area of research. Argumentation allows agents to harmonise two types of disagreement situations: internal, when the acquisition of new information (e.g., about the environment or about other agents) produces incoherences in the agents' mental state; and external, when agents that have different positions about a topic engage in a discussion. The focus of this paper is on the latter type of disagreement situations. In those settings, agents must be able to generate, select and send arguments to other agents that will evaluate them in their turn. An efficient way for agents to manage these argumentation abilities is by using case-based reasoning, which has been successfully applied to argumentation from its earliest beginnings. This reasoning methodology also allows agents to learn from their experiences and therefore, to improve their argumentation skills. This paper analyses the advantages of applying case-based reasoning to manage arguments in multi-agent systems dialogues, identifies open issues and proposes new ideas to tackle them.This work was partially supported by CONSOLIDERINGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under CICYT TIN2005-03395 and TIN2006-14630-C0301 projects.Heras Barberá, SM.; Julian Inglada, VJ.; Botti Navarro, VJ. (2010). Applying CBR to manage argumentation in MAS. International Journal of Reasoning-based Intelligent Systems. 2(2):110-117. https://doi.org/10.1504/IJRIS.2010.034906S1101172

Using Graph-Based Models in a Persuasive Social Recommendation System

© ACM 2015 This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM, In Proceedings of the 30th Annual ACM Symposium on Applied Computing (pp. 189-194).http://dx.doi.org/10.1145/2695664.2695732Nowadays, social networks have an enormous impact in the society generating a lot of useful information to be employed in new social applications. In this paper, we show how we have used a graph-based model to extract and model data in order to develop a Social Recommendation System which recommends recipes in a social network.This work was partially supported by the project MINE-CO/FEDER TIN2012-365686-C03-01 of the Spanish government and by the Spanish Ministry of Education, Culture and Sports under the Program for R&D Valorisation and Joint Resources VLC/CAMPUS, as part of the Campus of International Excellence Program (Ref. SP20140788).Palanca Cámara, J.; Heras Barberá, SM.; Jorge Cano, J.; Julian Inglada, VJ. (2015). Using Graph-Based Models in a Persuasive Social Recommendation System. ACM. https://doi.org/10.1145/2695664.2695732SDesel, J., Pernici, B., Weske, M. Mining Social Networks: Uncovering Interaction Patterns in Business Processes.Business Process Management, Berlin, vol. 3080, pp. 244--260 (2004)Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on KDE <b>17</b>(6) (2005) 734--749X. Zhou, Y. Xu, Y. Li, A. Josang, and C. Cox, "The state-of-the-art in personalized recommender systems for social networking,"Artificial Intelligence Review, vol. 37, no. 2, pp. 119--132, 2012.Ehrig M., "Ontology Alignment: Bridging the Semantic Gap,"Springer, 2007.Euzenat, J. and Shvaiko P., "Ontology matching,"Springer, Heidelberg (DE), 2007.Bleiholder, J., Naumann, F., "Data Fusion,"ACM Computing Surveys, 41(1):1--41, 2008.Halpin, H., Thomson, H., "Special Issue on Identify, Reference and the Web,"Int. Journal on Semantic Web and Information Systems, 4(2):1--72, 2008.I. Robinson, J. Webber, and E. Eifrem,Graph Databases. O'Reilly, 2013.M. Pazzani and D. Billsus,Content-Based Recommendation Systems, ser. LNCS. Springer-Verlag, 2007, vol. 4321, pp. 325--341.J. Schafer, D. Frankowski, J. Herlocker, and S. Sen,Collaborative Filtering Recommender Systems, ser. LNCS. Springer, 2007, v. 4321, pp. 291--324.R. Burke, "Hybrid Recommender Systems: Survey and Experiments,"User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331--370, 2002.C. Chesñevar, A. Maguitman, and M. González,Empowering Recommendation Technologies Through Argumentation. Springer, 2009, pp. 403--422.G. Linden, J. Hong, M. Stonebraker, and M. Guzdial:, "Recommendation Algorithms, Online Privacy and More,"Comm. of the ACM, vol. 52, no. 5, 2009.Khare, Rohit and Çelik, Tantek, "Microformats: a pragmatic path to the semantic web" in15th international conference on World Wide Web, ACM, 2006, pp. 865--866.Fogués, Ricard L and Such, Jose M and et al, "BFF: A tool for eliciting tie strength and user communities in social networking services", inInformation Systems Frontiers, Springer, 2013, pp. 1--13.S. Heras, V. Botti, and V. Julián. Argument-based agreements in agent societies.Neurocomputing, doi:10.1016/j.neucom.2011.02.022, 2011

Guidelines to apply CBR in Real-Time Multi-Agent Systems

[EN] In real-time Multi-Agent Systems, Real-Time Agents merge intelligent deliberative techniques with real-time reactive actions in a distributed environment. CBR has been successfully applied in Multi-Agent Systems as deliberative mechanism for agents. However, in the case of Real-Time Multi-Agent Systems the temporal restrictions of their Real-Time Agents make their deliberation process to be temporally bounded. Therefore, this paper presents a guide to temporally bound the CBR to adapt it to be used as deliberative mechanism for Real-Time Agents.This work was partially supported by CONSOLIDERINGENIO 2010 under grant CSD2007-00022 and by the Spanish government and GVA funds under TIN2006- 14630-C0301 and PROMETEO/2008/051 projects.Navarro Llácer, M.; Heras Barberá, SM.; Julian Inglada, VJ. (2009). Guidelines to apply CBR in Real-Time Multi-Agent Systems. Journal of Physical Agents. 3(3):39-43. https://doi.org/10.14198/JoPha.2009.3.3.07S39433