234,458 research outputs found

    Challenges for a CBR framework for argumentation in open MAS

    Full text link
    [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 BarberĂĄ, 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. , CortĂ©s 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.SĂžrmo, 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/s0269888904000098BrĂŒninghaus 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-xBrĂŒninghaus 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. , CortĂ©s 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. , CortĂ©s 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. , CortĂ©s 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. , CortĂ©s U. , SĂ nchez-MarrĂš 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.DĂ­az-Agudo, B., & GonzĂĄlez-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.BrĂŒninghaus 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. , GarcĂ­a 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-

    Designing Software Architectures As a Composition of Specializations of Knowledge Domains

    Get PDF
    This paper summarizes our experimental research and software development activities in designing robust, adaptable and reusable software architectures. Several years ago, based on our previous experiences in object-oriented software development, we made the following assumption: ‘A software architecture should be a composition of specializations of knowledge domains’. To verify this assumption we carried out three pilot projects. In addition to the application of some popular domain analysis techniques such as use cases, we identified the invariant compositional structures of the software architectures and the related knowledge domains. Knowledge domains define the boundaries of the adaptability and reusability capabilities of software systems. Next, knowledge domains were mapped to object-oriented concepts. We experienced that some aspects of knowledge could not be directly modeled in terms of object-oriented concepts. In this paper we describe our approach, the pilot projects, the experienced problems and the adopted solutions for realizing the software architectures. We conclude the paper with the lessons that we learned from this experience

    Compositional Reasoning for Explicit Resource Management in Channel-Based Concurrency

    Get PDF
    We define a pi-calculus variant with a costed semantics where channels are treated as resources that must explicitly be allocated before they are used and can be deallocated when no longer required. We use a substructural type system tracking permission transfer to construct coinductive proof techniques for comparing behaviour and resource usage efficiency of concurrent processes. We establish full abstraction results between our coinductive definitions and a contextual behavioural preorder describing a notion of process efficiency w.r.t. its management of resources. We also justify these definitions and respective proof techniques through numerous examples and a case study comparing two concurrent implementations of an extensible buffer.Comment: 51 pages, 7 figure

    Towards Intelligent Databases

    Get PDF
    This article is a presentation of the objectives and techniques of deductive databases. The deductive approach to databases aims at extending with intensional definitions other database paradigms that describe applications extensionaUy. We first show how constructive specifications can be expressed with deduction rules, and how normative conditions can be defined using integrity constraints. We outline the principles of bottom-up and top-down query answering procedures and present the techniques used for integrity checking. We then argue that it is often desirable to manage with a database system not only database applications, but also specifications of system components. We present such meta-level specifications and discuss their advantages over conventional approaches

    A model for continuous monitoring of patients with major depression in short and long term periods

    Get PDF
    The final publication is available at IOS Press through http://dx.doi.org/10.3233/THC-161289BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of disease burden by 2030. The prevalence of depression, all of its social and personal costs, and its recurrent characteristics, put heavy constraints on the ability of the public healthcare system to provide sufficient support for patients with depression. In this research, a model for continuous monitoring and tracking of depression in both short-term and long-term periods is presented. This model is based on a new qualitative reasoning approach. METHOD: This paper describes the patient assessment unit of a major depression monitoring system that has three modules: a patient progress module, based on a qualitative reasoning model; an analysis module, based on expert knowledge and a rules-based system; and the communication module. These modules base their reasoning mainly on data of the patient's mood and life events that are obtained from the patient's responses to specific questionnaires (PHQ-9, M.I.N.I. and Brugha). The patient assessment unit provides synthetic and useful information for both patients and physicians, keeps them informed of the progress of patients, and alerts them in the case of necessity. RESULTS: A set of hypothetical patients has been defined based on clinically possible cases in order to perform a complete scenario evaluation. The results that have been verified by psychiatrists suggest the utility of the platform. CONCLUSION: The proposed major depression monitoring system takes advantage of current technologies and facilitates more frequent follow-up of the progress of patients during their home stay after being diagnosed with depression by a psychiatrist.Peer ReviewedPostprint (author's final draft

    Building product suggestions for a BIM model based on rule sets and a semantic reasoning engine

    Get PDF
    The architecture, engineering and construction (AEC) industry today relies on different information systems and computational tools built to support and assist in the building design and construction. However, these systems and tools typically provide this support in isolation from each other. A good combination of these systems and tools is beneficial for a better coordination and information management. Semantic web technologies and a Linked Data approach can be used to fulfil this aim. In this paper, we indicate how these technologies can be applied for one particular objective, namely to check a building information model (BIM) and make suggestions for that model regarding the building elements. These suggestions are based on information obtained from different data sources, including a BIM model, regulations and catalogues of locally available building components. In this paper, we briefly discuss the results obtained in the application of this approach in a case study based on structural safety requirements
    • 

    corecore