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

Our System IDCBR-MAS: from the Modelisation by AUML to the Implementation under JADE Platform

This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learner’s traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors

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

Incorporating temporal-bounded CBR techniques in real-time agents

Nowadays, MAS paradigm tries to move Computation to a new level of abstraction: Computation as interaction, where large complex systems are seen in terms of the services they offer, and consequently in terms of the entities or agents providing or consuming services. However, MAS technology is found to be lacking in some critical environments as real-time environments. An interaction-based vision of a real-time system involves the purchase of a responsibility by any entity or agent for the accomplishment of a required service under possibly hard or soft temporal conditions. This vision notably increases the complexity of these kinds of systems. The main problem in the architecture development of agents in real-time environments is with the deliberation process where it is difficult to integrate complex bounded deliberative processes for decision-making in a simple and efficient way. According to this, this work presents a temporal-bounded deliberative case-based behaviour as an anytime solution. More specifically, the work proposes a new temporal-bounded CBR algorithm which facilitates deliberative processes for agents in real-time environments, which need both real-time and deliberative capabilities. The paper presents too an application example for the automated management simulation of internal and external mail in a department plant. This example has allowed to evaluate the proposal investigating the performance of the system and the temporal-bounded deliberative case-based behaviour. 2010 Elsevier Ltd. All rights reserved.This work is supported by TIN2006-14630-C03-01 projects of the Spanish government, GVPRE/2008/070 project, FEDER funds and CONSOLIDER-INGENIO 2010 under Grant CSD2007-00022.Navarro Llácer, M.; Heras Barberá, SM.; Julian Inglada, VJ.; Botti Navarro, VJ. (2011). Incorporating temporal-bounded CBR techniques in real-time agents. Expert Systems with Applications. 38(3):2783-2796. https://doi.org/10.1016/j.eswa.2010.08.070S2783279638

Case-based argumentation infrastructure for agent societies

In this work, we propose an infrastructure to develop and execute argumentative agents in an open multi-agent system. This infrastructure offers the necessary components to develop agents with argumentation capabilities, including the communication skills and the argumentation protocol, and it offers support for agent societies and their agents' social context. The main advantage of having this infrastructure is that it is possible to create agents with argumentation capabilities to resolve a specified problem. In the argumentation dialogue the agents try to reach an agreement about the best solution to apply for each proposed problem. The proposed infrastructure has been validated with a real example and it has been evaluated obtaining, with argumentation strategies, better performance than other reasoning approaches that do not include argumentation.Jordán Prunera, JM. (2011). Case-based argumentation infrastructure for agent societies. http://hdl.handle.net/10251/15362Archivo delegad

Case-Based strategies for argumentation dialogues in agent societies

[EN] In multi-agent systems, 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, especially when these systems become open, with heterogeneous agents dynamically entering or leaving the system. Therefore, agents willing to participate in this type of system will be required to include extra capabilities to explicitly represent and generate agreements on top of the simpler ability to interact. Furthermore, agents in multiagent systems can form societies, which impose social dependencies on them. These dependencies have a decisive influence in the way agents interact and reach agreements. Argumentation provides a natural means of dealing with conflicts of interest and opinion. Agents can reach agreements by engaging in argumentation dialogues with their opponents in a discussion. In addition, agents can take advantage of previous argumentation experiences to follow dialogue strategies and persuade other agents to accept their opinions. Our insight is that case-based reasoning can be very useful to manage argumentation in open multi-agent systems and devise dialogue strategies based on previous argumentation experiences. To demonstrate the foundations of this suggestion, this paper presents the work that we have done to develop case-based dialogue 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.This work is supported by the Spanish Government Grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO 2008/051].Heras Barberá, SM.; Jordan Prunera, JM.; Botti, V.; Julian Inglada, VJ. (2013). Case-Based strategies for argumentation dialogues in agent societies. Information Sciences. 223:1-30. doi:10.1016/j.ins.2012.10.007S13022

ProCLAIM: An argument-based model for monitoring decisions and revising guidelines knowledge in safety-critical environments

In this report we describe an argument-based model - ProCLAIM - for monitoring agents' decisions in safety-critical environments intended, on the one hand, to prevent agents from undertaking decisions that do not comply with the established guidelines given by the domain, and on the other hand, by enabling agents to argue over their intended decisions, to allow agents to exceptionally undertake decisions that violate the existing guidelines when their given arguments supporting the decisions are accepted. Furthermore, ProCLAIM defines a Case-Based Reasoning component intended for revising the guidelines knowledge that control the agents' decisions. Namely, the arguments given by the agents to support their decisions are stored in a case base and eventually reused in order to revise the Guideline Knowledge so that it will accept new decisions shown to be successful despite of violating the guidelines, and at the same time, to reject decisions that in spite of being complaint with these guidelines have shown to be unsuccessful. We believe and aim to show in this report that ProCLAIM provides a number of interesting theoretical innovations in artificial intelligence which are motivated with valuable practical applications.Postprint (published version

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. 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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