Jaume Jordán

Argue to agree: A case-based argumentation approach

The capability of reaching agreements is a necessary feature that large computer systems where agents interoperate must include. In these systems, agents represent self-motivated entities that have a social context, including dependency relations among them, and different preferences and beliefs. Without agreement there is no cooperation and thus, complex tasks which require the interaction of agents with different points of view cannot be performed. In this work, we propose a case-based argumentation approach for Multi-Agent Systems where agents reach agreements by arguing and improve their argumentation skills from experience. A set of knowledge resources and a reasoning process that agents can use to manage their positions and arguments are presented. These elements are implemented and validated in a customer support application.

A Multi-agent Planning Approach for the Generation of Personalized Treatment Plans of Comorbid Patients

This work addresses the generation of a personalized treatment plan from multiple clinical guidelines, for a patient with multiple diseases (comorbid patient), as a multi-agent cooperative planning process that provides support to collaborative medical decision-making. The proposal is based on a multi-agent planning architecture in which each agent is capable of (1) planning a personalized treatment from a temporal Hierarchical Task Network (HTN) representation of a single-disease guideline, and (2) coordinating with other planning agents by both sharing disease specific knowledge, and resolving the eventual conflicts that may arise when conciliating different guidelines by merging single-disease treatment plans. The architecture follows a life cycle that starting from a common specification of the main high-level steps of a treatment for a given comorbid patient, results in a detailed treatment plan without harmful interactions among the single-disease personalized treatments.

Case-based strategies for argumentation dialogues in agent societies

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 multi-agent 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.

An argumentation framework for supporting agreements in agent societies applied to customer support

This work presents a system for customer support that integrates case-based reasoning functionalities with an argumentation framework for agent societies. This integration allows to automatically engage in agreement processes to decide the best solution to apply to solve an incidence that has been received in a call center. In this way, the quality of the response would be increased and the company running the call center can take advantage over its competitors in the market.

Argumentation Schemes for Events Suggestion in an e-Health Platform

In this work, we propose the introduction of persuasion techniques that guide the users into interacting with the Ambient Assisted Living framework iGenda. It is a cognitive assistant that manages active daily living activities, monitors user’s health condition, and creates a social network between users via mobile devices. The objective is to be inserted in a healthcare environment and to provide features like adaptive interfaces, user profiling and machine learning processes that enhance the usage experience. The inclusion of a persuasive architecture (based on argumentation schemes) enables the system to provide recommendations to the users that fit their profile and interests, thus increases the chance of a positive interaction.

Using Genetic Algorithms to Optimize the Location of Electric Vehicle Charging Stations

The creation of a suitable charging infrastructure for electric vehicles (EV) is one of the main challenges to increase the adoption of this new vehicle technologies. In this article, we present a Multi-Agent System (MAS) that performs an analysis of a set of possible configurations for the location of EV charging stations in a city. To estimate the best configurations, the proposed MAS considers data from heterogeneous sources such as traffic, social networks, population, etc. Based on this information, the agents are able to analyze a large set of configurations using a genetic algorithm that optimizes the configurations taking into account a utility function.

A better-response strategy for self-interested planning agents

When self-interested agents plan individually, interactions that prevent them from executing their actions as planned may arise. In these coordination problems, game-theoretic planning can be used to enhance the agents’ strategic behavior considering the interactions as part of the agents’ utility. In this work, we define a general-sum game in which interactions such as conflicts and congestions are reflected in the agents’ utility. We propose a better-response planning strategy that guarantees convergence to an equilibrium joint plan by imposing a tax to agents involved in conflicts. We apply our approach to a real-world problem in which agents are Electric Autonomous Vehicles (EAVs). The EAVs intend to find a joint plan that ensures their individual goals are achievable in a transportation scenario where congestion and conflicting situations may arise. Although the task is computationally hard, as we theoretically prove, the experimental results show that our approach outperforms similar approaches in both performance and solution quality.

Using Argumentation Schemes for a Persuasive Cognitive Assistant System

The iGenda framework is a cognitive assistant that helps care-receivers and caregivers in the management of their agendas. One of the problems detected in systems of this kind is the lack of user engagement. This engagement can be improved through the application of persuasion techniques in order to convince users to act in a specific way. According to this, this paper presents a new architecture that will allow the system to select and recommend activities that potentially best suits to the users’ interests based on argumentation techniques.

An Infrastructure for Argumentative Agents

Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows representation of more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open multiagent system. It offers tools to develop agents with argumentation capabilities. It also offers support for agent societies and their social context. The infrastructure is publicly available. Also, it has been implemented in an application scenario where argumentative agents try to reach an agreement about the best solution to solve a problem reported to the system.

Case-Based argumentation infrastructure for agent societies

Despite the important advances in the argumentation theory, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows to represent more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open Multi-Agent System. This infrastructure offers the tools to develop agents with argumentation capabilities, including the communication skills and the argumentation protocol. It also offers support for agent societies and their social context.