Capturing the behaviour of inter-agent dialogues

A multiagent system (MAS) is made up of multiple interacting autonomous agents. It can be viewed as a society in which each agent performs its activity, cooperating to achieve common goals, or competing for them. Thus, every agent has the ability to do social interactions with other agents establishing dialogues via some kind of agent-communication language, under some communication protocol [13]. Argumentation has been used to model several kind of dialogues in multi-agents systems, such as negotiation or coordination [1, 7, 8, 5, 9]. Our current research activities are related to the use of argumentation in agent’s interaction, as a form of social dialogue. According to [15], dialogues can be classified in negotiation, where there is a conflict of interests, persuasion where there is a conflict of opinion or beliefs, indagation where there is a need for an explanation or proof of some proposition, deliberation or coordination where there is a need to coordinate goals and actions, and one special kind of dialogue called eristic based on personal conflicts. Except the last one, all this dialogues may exist in multi-agents systems as part of social activities among agents. We also study the use of argumentation formalisms to model the internal process of reasoning of an agent, often called monologues. Our aim is to define an abstract argumentation framework to capture the behaviour of these different dialogues. We are not interested in the logic used to construct arguments. Our formulation completely abstracts from the internal structure of the arguments, considering them as moves made in a dialogue. We also consider multiagent systems as a set of multiple interacting autonomous agents.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI

Dialoguing DeLP-based agents

A multi-agent system is made up of multiple interacting autonomous agents. It can be viewed as a society in which each agent performs its activity cooperating to achieve common goals, or competing for them. They establish dialogues via some kind of agent-communication language, under some communication protocol. We think argumentation is suitable to model several kind of dialogues in multi-agents systems. In this paper we define dialogues and persuasion dialogues between two agents using Defeasible Logic Programs as a knowledge base, together with an algorithm defining how this dialogue may be engaged. We also show an indication of how an agent could use opponent’s information for its own benefit.Eje: AgentesRed de Universidades con Carreras en Informática (RedUNCI

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. 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Arguing and negotiating in the presence of social influences

Postprin

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

Dynamic argumentation in UbiGDSS

"First Online: 17 August 2017"Supporting and representing the group decision-making process is a complex task that requires very specific aspects. The current existing argumentation models cannot make good use of all the advantages inherent to group decision-making. There is no monitoring of the process or the possibility to provide dynamism to it. These issues can compromise the success of Group Decision Support Systems if those systems are not able to provide freedom and all necessary mechanisms to the decision-maker. We investigate the use of argumentation in a completely new perspective that will allow for a mutual understanding between agents and decision-makers. Besides this, our proposal allows to define an agent not only according to the preferences of the decisionmaker but also according to his interests towards the decision-making process. We show that our definition respects the requirements that are essential for groups to interact without limitations and that can take advantage of those interactions to create valuable knowledge to support more and better.This work has been supported by COMPETE Programme (operational programme for competitiveness) within project POCI-01-0145-FEDER-007043, by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Projects UID/CEC/00319/2013, UID/EEA/00760/2013, and the João Carneiro PhD grant with the reference SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio

Argumentation dialogues in web-based GDSS: an approach using machine learning techniques

Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais. Normalmente, no contexto organizacional, as decisões são tomadas em grupo. Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver. Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo: a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre outras). Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio, formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web. No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be related to everyday problems, or they can be related to more complex issues, such as organizational issues. Normally, in the organizational context, decisions are made in groups. Group Decision Support Systems have been studied over the past decades with the aim of improving the support provided to decision-makers in the most diverse situations and/or problems to be solved. There are two main approaches to implementing Group Decision Support Systems: the classical approach, based on the mathematical aggregation of the preferences of the different elements of the group, and the approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others). Current argumentation-based Group Decision Support Systems can generate an enormous amount of data. The objective of this research work is to study and develop models using automatic learning techniques to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically, it is intended to create models to analyze, classify and process these data, enhancing the generation of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in this way the achievement of consensus among the members of the group. Based on the literature study and the open challenges in this domain, the following research hypothesis was formulated - It is possible to use machine learning techniques to support argumentative dialogues in web-based Group Decision Support Systems. As part of the work developed, supervised classification algorithms were applied to a data set containing arguments extracted from online debates, creating an argumentative sentence classifier that can automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making. A dynamic clustering model was developed to organize conversations based on the arguments used. In addition, a web-based Group Decision Support System was proposed that makes it possible to support groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of preferences, intentions, and interests of each decision-maker. This web-based decision support system includes dashboards of intelligent reports that are generated through the results of the work achieved by the previous models already mentioned. The achievement of each objective allowed validation of the identified research questions and thus responded positively to the defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018

Abduction and Dialogical Proof in Argumentation and Logic Programming

We develop a model of abduction in abstract argumentation, where changes to an argumentation framework act as hypotheses to explain the support of an observation. We present dialogical proof theories for the main decision problems (i.e., finding hypothe- ses that explain skeptical/credulous support) and we show that our model can be instantiated on the basis of abductive logic programs.Comment: Appears in the Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014