An empirical study of argumentation schemes in deliberative dialogue

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Argumentation schemes for collaborative planning

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Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

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-

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Using Computational Agents to Design Participatory Social Simulations

In social science, the role of stakeholders is increasing in the development and use of simulation models. Their participation in the design of agent-based models (ABMs) has widely been considered as an efficient solution to the validation of this particular type of model. Traditionally, "agents" (as basic model elements) have not been concerned with stakeholders directly but via designers or role-playing games (RPGs). In this paper, we intend to bridge this gap by introducing computational or software agents, implemented from an initial ABM, into a new kind of RPG, mediated by computers, so that these agents can interact with stakeholders. This interaction can help not only to elicit stakeholders' informal knowledge or unpredicted behaviours, but also to control stakeholders' focus during the games. We therefore formalize a general participatory design method using software agents, and illustrate it by describing our experience in a project aimed at developing agent-based social simulations in the field of air traffic management.Participatory Social Simulations, Agent-Based Social Simulations, Computational Agents, Role-Playing Games, Artificial Maieutics, User-Centered Design

ProCLAIM: an argument-based model for deliberating over safety critical actions

In this Thesis we present an argument-based model – ProCLAIM – intended to provide a setting for heterogeneous agents to deliberate on whether a proposed action is safe. That is, whether or not a proposed action is expected to cause some undesirable side effect that will justify not to undertake the proposed action. This is particularly relevant in safetycritical environments where the consequences ensuing from an inappropriate action may be catastrophic. For the practical realisation of the deliberations the model features a mediator agent with three main tasks: 1) guide the participating agents in what their valid argumentation moves are at each stage of the deliberation; 2) decide whether submitted arguments should be accepted on the basis of their relevance; and finally, 3) evaluate the accepted arguments in order to provide an assessment on whether the proposed action should or should not be undertaken, where the argument evaluation is based on domain consented knowledge (e.g guidelines and regulations), evidence and the decision makers’ expertise. To motivate ProCLAIM’s practical value and generality the model is applied in two scenarios: human organ transplantation and industrial wastewater. In the former scenario, ProCLAIM is used to facilitate the deliberation between two medical doctors on whether an available organ for transplantation is or is not suitable for a particular potential recipient (i.e. whether it is safe to transplant the organ). In the later scenario, a number of agents deliberate on whether an industrial discharge is environmentally safe.En esta tesis se presenta un modelo basado en la Argumentación –ProCLAIM– cuyo n es proporcionar un entorno para la deliberación sobre acciones críticas para la seguridad entre agentes heterogéneos. En particular, el propósito de la deliberación es decidir si los efectos secundario indeseables de una acción justi can no llevarla a cabo. Esto es particularmente relevante en entornos críticos para la seguridad, donde las consecuencias que se derivan de una acción inadecuada puede ser catastró cas. Para la realización práctica de las deliberaciones propuestas, el modelo cuenta con un agente mediador con tres tareas principales: 1) guiar a los agentes participantes indicando cuales son las líneas argumentación válidas en cada etapa de la deliberación; 2) decidir si los argumentos presentados deben ser aceptadas sobre la base de su relevancia y, por último, 3) evaluar los argumentos aceptados con el n de proporcionar una valoración sobre la seguridad de la acción propuesta. Esta valoración se basa en guías y regulaciones del dominio de aplicación, en evidencia y en la opinión de los expertos responsables de la decisión. Para motivar el valor práctico y la generalidad de ProCLAIM, este modelo se aplica en dos escenarios distintos: el trasplante de órganos y la gestión de aguas residuales. En el primer escenario el modelo se utiliza para facilitar la deliberación entre dos médicos sobre la viabilidad del transplante de un órgano para un receptor potencial (es decir, si el transplante es seguro). En el segundo escenario varios agentes deliberan sobre si los efectos de un vertido industrial con el propósito de minimizar su impacto medioambiental