Agents that learn how to generate arguments from other agents

Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rulesfor argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to dierent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation

Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, must decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection policy. Following this policy, the agent observes some factors of the negotiation context, for instance: trust in the opponent and expected utility of the negotiated agreement, among others. Usually, argument selection policies are dened statically. However, as the negotiation context varies from a negotiation to another, dening a static selection policy it is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the dierent negotiation contexts as the agent´s experience increases. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection eciency by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment, and obtained promising results in both.Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentin

Agents That Learn What Argument to Select In Argumentation-Based Negotiations

Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, has to decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection criterion. For this task, the agent observes some factors of the negotiation context, for instance trust in the opponent, expected utility, among others. Usually, argument selection mechanisms are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection mechanism it is not useful. For this reason, we present in this paper a novel approach to personalize argument selection mechanisms in the context of argumentation-based negotiation. The selection mechanism defines a set of preferences that determine how preferable it is to utter an argument in a given context. Our approach maintains a hierarchy of preferences in order to learn new preferences and update the existing ones as the agent experience increases. We tested this approach in a simulated multiagent system and obtained promising results.Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Group recommender systems: A multi-agent solution

Providing recommendations to groups of users has become a promising research area, since many items tend to be consumed by groups of people. Various techniques have been developed aiming at making recommendations to a group as a whole. Most works use aggregation techniques to combine preferences, recommendations or profiles. However, satisfying all group members in an even way still remains as a challenge. To deal with this problem, we propose an extension of a multi-agent approach based on negotiation techniques for group recommendation. In the approach, we use the multilateral Monotonic Concession Protocol (MCP) to combine individual recommendations into a group recommendation. In this work, we extend the MCP protocol to allow users to personalize the behavior of the agents. This extension was evaluated in two different domains (movies and points of interest) with satisfactory results. We compared our approach against different baselines, namely: a preference aggregation algorithm, a recommendation aggregation algorithm, and a simple one-step negotiation. The results show evidence that, when using our negotiation approach, users in the groups are more uniformly satisfied than with traditional aggregation approaches.Fil: Villavicencio, Christian Paulo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Using negotiation for group recommendation: a user-study on the movies domain

Providing recommendations to groups of users has become popular in many applications today. Even though there are several group recommendation techniques, the generation of recommendations that satisfy the group members in an even way remains a challenge. Because of this, we have developed a multi-agent approach called MAGReS that relies on negotiation techniques to improve group recommendations. Our approach was tested (on the movies domain) using synthetic data with satisfactory results. Given that the results when using synthetic data may sometimes differ with reality, we decided to assess MAGReS using data from real users. The results obtained showed firstly that, in comparison with the recommendations produced by a traditional approach, the recommendations of MAGReS produce a greater level of satisfaction to the group, and secondly that the proposed approach was able to predict more accurately the satisfaction levels of the group members. Finally, we could obtain some preliminary feedback regarding the explanations provided by the recommender system.Fil: Villavicencio, Christian Paulo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Whom should I persuade during a negotiation? An approach based on social influence maximization

During a negotiation, an agent must make several key decisions in order to achieve a profitable agreement. When the negotiation is carried out in a social context, agents can use persuasion, besides the traditional exchange of concessions. To carry out the persuasion and make concessions, the agents must employ resources that are usually scarce. For this reason, the agents should carefully decide which opponent they should persuade to maximise their profit, especially when the negotiation involves multiple parties. To make this decision, we propose that the agents should persuade the opponents with a high influence on the other agents involved in the negotiation. Therefore, we represent a negotiation context as a social influence maximization problem and solve it under a model that learns how influence flows in a network by analyzing historical information. This allows an agent to determine what opponents exert the highest influence. Finally, the agent uses this information to decide which opponent to persuade during the negotiation. Experimental results showed that the agreement rate increased when agents applied this approach.Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Building user argumentative models

Knowing how a user builds his/her arguments during a discussion gives useful advantages if we want to assist the user or analyse his/her argumentative skills. This paper presents a novel mechanism to build user argumentative models, which captures the argumentative style to generate arguments. To this end, we observe how users generate arguments, and apply a generalised association rules algorithm to discover rules for argument generation. These rules depict the argumentative style of the user. They are composed of an antecedent, which represents the conditions to build an argument, and a consequent, which represents such argument. To evaluate this proposal, we show results obtained in the domain of meeting scheduling. We discovered interesting rules from a group of users discussing in that domain, and checked that about 60% of the arguments that users had generated in a test situation can be also generated from the rules previously learnt, at least partially. Finally, although this work focuses on modelling users' argumentative style, we discuss how this promising approach could be applied in different knowledge domains.Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Amandi, Analia Adriana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Influence me! Predicting links to influential users

In addition to being in contact with friends, online social networks are commonly used as a source of information, suggestions and recommendations from members of the community. Whenever we accept a suggestion or perform any action because it was recommended by a “friend”, we are being influenced by him/her. For this reason, it is useful for users seeking for interesting information to identify and connect to this kind of influential users. In this context, we propose an approach to predict links to influential users. Compared to approaches that identify general influential users in a network, our approach seeks to identify users who might have some kind of influence to individual (target) users. To carry out this goal, we adapted an influence maximization algorithm to find new influential users from the set of current influential users of the target user. Moreover, we compared the results obtained with different metrics for link prediction and analyzed in which context these metrics obtained better results.Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Armentano, Marcelo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Influence-based approach to market basket analysis

In this article, we propose an approach to market basket analysis based on the notion of social influence. While traditional market basket analysis looks for combinations of products that frequently co-occur in transactions, we seek to find a set of influential products that, if bought by a customer, will increase the sales volume of the shop. We believe that customers who purchase influential products would also be influenced to purchase other products. We validated our approach with two real-world datasets collected from online shoppings and one dataset collected from a supermarket concluding that influential products identified by our approach increase the influence spread with respect to different baselines: best-selling, highest centrality, frequent sequence initiator, and most promoted products.Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Armentano, Marcelo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin