Defining agents’ behaviour for negotiation contexts

Agents who represent participants in the group decision-making context require a certain number of individual traits in order to be successful. By using argumentation models, agents are capable to defend the interests of those who they represent, and also justify and support their ideas and actions. However, regardless of how much knowledge they might hold, it is essential to define their behaviour. In this paper (1) is presented a study about the most important models to infer different types of behaviours that can be adapted and used in this context, (2) are proposed rules that must be followed to affect positively the system when defining behaviours and (3) is proposed the adaptation of a conflict management model to the context of Group Decision Support Systems. We propose one approach that (a) intends to reflect a natural way of human behaviour in the agents, (b) provides an easier way to reach an agreement between all parties involved and (c) does not have high configuration costs to the participants. Our approach will offer a simple yet perceptible configuration tool that can be used by the participants and contribute to more intelligent communications between agents and makes possible for the participants to have a better understanding of the types of interactions experienced by the agents belonging to the system.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEISII/1386/2012) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) with the João Carneiro PhD grant with the reference SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio

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

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

Dealing with similarity in argumentation

Le raisonnement argumentatif est basé sur la justification d'une conclusion plausible par des arguments en sa faveur. L'argumentation est un modèle prometteur pour raisonner avec des connaissances incertaines ou incohérentes, ou, plus généralement de sens communs. Ce modèle est basé sur la construction d'arguments et de contre-arguments, la comparaison de ces arguments et enfin l'évaluation de la force de chacun d'entre eux. Dans cette thèse, nous avons abordé la notion de similarité entre arguments. Nous avons étudié deux aspects : comment la mesurer et comment la prendre en compte dans l'évaluation des forces. Concernant le premier aspect, nous nous sommes intéressés aux arguments logiques, plus précisément à des arguments construits à partir de bases de connaissances propositionnelles. Nous avons commencé par proposer un ensemble d'axiomes qu'une mesure de similarité entre des arguments logiques doit satisfaire. Ensuite, nous avons proposé différentes mesures et étudié leurs propriétés. La deuxième partie de la thèse a consisté à définir les fondements théoriques qui décrivent les principes et les processus impliqués dans la définition d'une méthode d'évaluation des arguments prenant en compte la similarité. Une telle méthode calcule la force d'un argument sur la base de forces de ses attaquants, des similarités entre eux, et d'un poids initial de l'argument. Formellement, une méthode d'évaluation est définie par trois fonctions dont une, nommée "fonction d'ajustement", qui s'occupe de réajuster les forces des attaquants en fonction de leur similarité. Nous avons proposé des propriétés que doivent satisfaire les trois fonctions, ensuite nous avons défini une large famille de méthodes et étudié leurs propriétés. Enfin, nous avons défini différentes fonctions d'ajustement, montrant ainsi que différentes stratégies peuvent être suivies pour contourner la redondance pouvant exister entre les attaquants d'un argument.Argumentative reasoning is based on justifying a plausible conclusion with arguments in its favour. Argumentation is a promising model for reasoning with uncertain or inconsistent knowledge, or, more generally, common sense. This model is based on the construction of arguments and counter-arguments, the comparison of these arguments and finally the evaluation of the strength of each of them. In this thesis, we have tackled the notion of similarity between arguments. We have studied two aspects: how to measure it and how to take it into account in the evaluation of strengths. With regards to the first aspect, we were interested in logical arguments, more precisely in arguments built from propositional knowledge bases. We started by proposing a set of axioms that a similarity measure between logical arguments must satisfy. Then, we proposed different measures and studied their properties. The second part of the thesis was focused on defining the theoretical foundations that describe the principles and processes involved in the definition of an evaluation method for arguments, which takes similarity into account. Such a method computes the strength of an argument based on the strengths of its attackers, the similarities between them, and an initial weight of the argument. Formally, an evaluation method is defined by three functions, one of which (called the adjustment function) is concerned with readjusting the strengths of the attackers according to their similarity. We have proposed properties that the three functions must satisfy, after which we have defined a large family of methods and studied their properties. At last, we have defined different adjustment functions, showing that different strategies can be applied to avoid the redundancy that can exist between the attackers of an argument

Desenvolvimento de uma framework de argumentação Para apoio à tomada de decisão ubíqua

Os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) surgiram com o objetivo de apoiar um conjunto de decisores no processo de tomada de decisão. Uma das abordagens mais comuns na literatura para a implementação dos SADG é a utilização de Sistemas Multi-Agente (SMA). Os SMA permitem refletir com maior transparência o contexto real, tanto na representação que cada agente faz do decisor que representa como no formato de comunicação utilizado. Com o crescimento das organizações, atualmente vive-se uma viragem no conceito de tomada de decisão. Cada vez mais, devido a questões como: o estilo de vida, os mercados globais e o tipo de tecnologias disponíveis, faz sentido falar de decisão ubíqua. Isto significa que o decisor deverá poder utilizar o sistema a partir de qualquer local, a qualquer altura e através dos mais variados tipos de dispositivos eletrónicos tais como tablets, smartphones, etc. Neste trabalho é proposto um novo modelo de argumentação, adaptado ao contexto da tomada de decisão ubíqua para ser utilizado por um SMA na resolução de problemas multi-critério. É assumido que cada agente poderá utilizar um estilo de comportamento que afeta o modo como esse agente interage com outros agentes em situações de conflito. Sendo assim, pretende-se estudar o impacto da utilização de estilos de comportamento ao longo do processo da tomada de decisão e perceber se os agentes modelados com estilos de comportamento conseguem atingir o consenso mais facilmente quando comparados com agentes que não apresentam nenhum estilo de comportamento. Pretende-se ainda estudar se o número de argumentos trocados entre os agentes é proporcional ao nível de consenso final após o processo de tomada de decisão. De forma a poder estudar as hipóteses de investigação desenvolveu-se um protótipo de um SADG, utilizando um SMA. Desenvolveu-se ainda uma framework de argumentação que foi adaptada ao protótipo desenvolvido. Os resultados obtidos permitiram validar as hipóteses definidas neste trabalho tendo-se concluído que os agentes modelados com estilos de comportamento conseguem na maioria das vezes atingir um consenso mais facilmente comparado com agentes que não apresentam nenhum estilo de comportamento e que o número de argumentos trocados entre os agentes durante o processo de tomada de decisão não é proporcional ao nível de consenso final.The Group Decision Support Systems (GDSS) emerged with the main goal to support a group of decision-makers throughout the decision process. One of the most common approaches in literature for GDSS is the use of Multi-Agent Systems (MAS). MAS reflect the real context of decision-making in the way the agent represents the decision-maker and also in the format it uses to communicate with other agents. Nowadays, with the expansion of organizations we are seeing a change in the concept of decision. Ubiquitous decision is being discussed more theses days due to questions such as lifestyle, global markets, and available technologies. This means that the decision-maker should be allowed to use the system from anywhere, at any time and through any sort of electronic device such as tablet, smartphone, etc. In this work it is proposed a new argumentation model adapted to the context of ubiquitous decision-making and that will be used by a MAS in the resolution of multi-criteria problems. It is assumed that any agent is allowed to use a behaviour style and that will affect the way that agent interacts with other agents in conflicting situations. Therefore the aim of this work is to study the impact of the use of behaviour styles throughout the decision-making process and to understand if agents modeled with a behaviour style will reach a consensus easier compared with agents that do not use any behaviour style. It is also intended to study if the number of arguments exchanged between agents is proportional to the final consensus level obtained. In order to study these hypothesis a prototype for a GDSS was developed using a MAS. An argumentation framework was also developed and was adapted to the prototype. The obtained results allowed to validate all the hypothesis defined in this work and it was concluded that agents modeled with behaviour styles will almost always reach a consensus easier compared with agents without behaviour styles and the number of arguments exchanged during the decision-making process is not proportional to the final consensus level

A Framework for Argumentation-Based Agent Negotiation in Uncertain Settings

Automated negotiation technologies are being increasingly used in business applications, especially in the e-Commerce domain. Argumentation-Based Negotiation (ABN), among the existing approaches, has been distinguished as a powerful approach to automated negotiation due to its ability to provide more sophisticated information (arguments) that justifies and supports agents’ proposals in order to mutually influence their preference relations on the set of offers, and consequently on the negotiation outcome. During the recent years, argumentation-based negotiation has received a considerable attention in the area of agent communication. However, current proposals are mostly concerned with presenting protocols for showing how agents can interact with each other, and how arguments and offers can be generated, evaluated and exchanged under the assumption of certainty. Therefore, none of these proposals is directly targeting the agents’ uncertainty about the selection of their moves nor designing the appropriate negotiation strategies based on this uncertainty in order to help the negotiating agents better make their decisions in the negotiation settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. In this thesis, we tackle the aforementioned problems by advocating an Argumentation-Based Agent Negotiation (ABAN) framework that is capable of handling the problem of agents’ uncertainty during the negotiation process. We begin by proposing an argumentation framework enriched with a new element called agent’s uncertainty as an important parameter in the agent theory to allow negotiating agents to decide which moves to play and reason about the selection of these moves under the assumption of uncertainty. Then, a method for agents’ uncertainty assessment is presented. In particular, we use Shannon entropy to assess agent’s uncertainty about their moves at each dialogue step as well as for the whole dialogue. Negotiation strategies and agent profiles issues are also explored and a methodology for designing novel negotiation strategies and agent profiles under the assumption of uncertainty is developed. Moreover, two important outcome properties namely, completeness and Nash equilibrium are discussed. Finally, the applicability of our framework is explored through several scenarios of the well-known Buyer/Seller case study. The obtained empirical results confirm the effectiveness of using our uncertainty-aware techniques and demonstrate the usefulness of using such techniques in argumentation-based negotiations

Argumentative alternating offers

International audienceThis paper presents an argumentative version of the well known alternating offers negotiation protocol. The negotiation mechanism is based on an abstract preference based argumentation framework where both epistemic and practical arguments are taken into consideration in order to decide about different strategic issues. Such issues are the offer that is proposed at each round, acceptance or refusal of an offer, concession or withdrawal from the negotiation. The argumentation framework shows clearly how offers are linked to practical arguments that support them, as well as how the latter are influenced by epistemic arguments. Moreover it illustrates how agents' argumentative theories evolution, due to the exchange of arguments, influences the negotiation outcome. Finally, a generic algorithm that implements a concession based negotiation strategy is presented

Argumentative Alternating Offers

This paper presents an argumentative version of the well known alternating offers negotiation protocol. The negotiation mechanism is based on an abstract preference based argumentation framework where both epistemic and practical arguments are taken into consideration in order to decide about different strategic issues. Such issues are the offer that is proposed at each round, acceptance or refusal of an offer, concession or withdrawal from the negotiation. The argumentation framework shows clearly how offers are linked to practical arguments that support them, as well as how the latter are influenced by epistemic arguments. Moreover it illustrates how agents’argumentative theories evolution, due to the exchange of arguments, influences the negotiation outcome. Finally, a generic algorithm that implements a concession based negotiation strategy is presented