An Adaptive Trust Model Based on Fuzzy Logic

In cooperative environments is common that agents delegate tasks to each other to achieve their goals since an agent may not have the capabilities or resources to achieve its objectives alone. However, to select good partners, the agent needs to deal with information about the abilities, experience, and goals of their partners. In this situation, the lack or inaccuracy of information may affect the agent's judgment about a given partner; and hence, increases the risk to rely on an untrustworthy agent. Therefore, in this work, we present a trust model that combines different pieces of information, such as social image, reputation, and references to produce more precise information about the characteristics and abilities of agents. An important aspect of our trust model is that it can be easily configured to deal with different evaluation criteria. For instance, as presented in our experiments, the agents are able to select their partners by availability instead of the expertise level. Besides, the model allows the agents to decide when their own opinions about a partner are more relevant than the opinions received from third parties, and vice-versa. Such flexibility can be explored in dynamic scenarios, where the environment and the behavior of the agents might change constantly

Online Handbook of Argumentation for AI: Volume 2

Editors: Federico Castagna, Francesca Mosca, Jack Mumford, Stefan Sarkadi and Andreas Xydis.This volume contains revised versions of the papers selected for the second volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI

Calculating rehtorical arguments strength and their utilization in dialogues of persuasive negotiation in multiagent systems

A negotiation between agents is called persuasive when the proposals are backed by rhetorical arguments (threats, rewards, or appeals), whose role is to try to persuade the opponent agent to accept the proposal more readily. This thesis tackles the problem of calculating the strength value of these kinds of arguments. In the related work, the strength value of a rhetorical argument is represented by a vector of two elements: the value of the uncertainty of the beliefs that make up the argument and the value of the importance of the opponent’s goal. Nevertheless, there is a need of a further analysis of these components and of the characteristics of the participant agents that may impact on the strength value. Therefore, the objective of this work is to study these kinds of arguments and to propose a more expressive model for calculating their strength values. This thesis contains three main parts. The first one concerns the design of an agent architecture that is based on the goal processing model proposed by Castelfranchi and Paglieri, which can be considered an extension of the Beliefs-Desires-Intentions (BDI) model. In this model, the goals go through four stages of filtering from being mere desires until they become an intention. We propose an argumentation-based formalization of this model, which means that the passage of the goals from one stage to the next is supported by arguments. The second part of this thesis concerns the strength value calculation model. First of all, the architecture of the negotiation agents and the logical definitions of each kind of rhetorical argument are presented. After that, the criteria that are taken into account for the strength calculation are presented. Thus, besides considering the importance of the opponent’s goal, we also consider the effectiveness of this goal and the credibility of the participant agents. The effectiveness of the opponent’s goal is calculated based on its status – according to the model of Castelfranchi and Paglieri – and the kind of rhetorical argument it makes up. The last part presents a set of experiments that aims to empirically evaluate the proposed model. With this purpose, firstly, a negotiation model that rules the behavior of the participant agents during the dialogue is presented. The experiments evaluate the efficiency of the proposal by comparing it with the closest proposal found in literature. The results demonstrate that the proposed model is more efficient in terms of number of negotiation cycles, number of exchanged arguments during the negotiation, and the number of achieved agreements.A negociação entre agentes inteligentes é chamada de persuasiva quando as propostas são apoiadas por argumentos retóricos (ameaças, recompensas ou apelações). Esta tese aborda o problema de cálculo da força destes tipos de argumentos, cujo papel é tentar persuadir o agente oponente a aceitar as propostas enviadas mais rapidamente. Nos trabalhos relacionados, o valor da força de um argumento retórico é representado por um vetor de dois elementos: o valor da incerteza das crenças que constituem o argumento e o valor da importância do objetivo do oponente. No entanto, existe uma necessidade de uma análise mais profunda destes componentes e das características dos participantes que podem influenciar no valor da força. Portanto, o objetivo deste trabalho é estudar estes tipos de argumentos e fornecer um modelo de cálculo da força que seja mais expressivo. Esta tese contém três partes principais. A primeira foca-se na delineação de uma arquitetura de agentes que é baseada no modelo de processamento de objetivos definido por Castelfranchi e Paglieri, o qual pode ser considerado uma extensão do modelo Crenças-Desejos-Intenções. Neste modelo, os objetivos passam por quatro etapas de filtragem, nas quais um objetivo começa em um estado adormecido e vira uma intenção ao passar a última etapa. O trabalho apresenta uma formalização computacional deste modelo baseada em argumentação, onde o avanço dos objetivos de uma etapa para outra é suportada por argumentos. A segunda parte desta tese foca-se no modelo de cálculo da força de argumentos retóricos. Primeiramente, apresenta-se a arquitetura dos agentes negociadores e as definições lógicas de ameaça, recompensa, e apelação. Em seguida, são apresentados os critérios que são utilizados no modelo de cálculo, tais como a importância do objetivo do oponente, a efetividade de dito objetivo e a credibilidade dos agentes participativos. A efetividade do objetivo é calculada tomando como base o estado deste –segundo o modelo de Castelfranchi e Paglieri– e o tipo de argumento retórico. A última parte apresenta um conjunto de experimentos que visam avaliar empiricamente o modelo de cálculo proposto. Com este propósito, primeiramente apresenta-se um modelo de negociação que rege o comportamento dos agentes participantes durante o diálogo. Os experimentos avaliam a eficiência da proposta, comparando-a com a proposta mais próxima encontrada na literatura. Os resultados demonstram que o modelo proposto é mais eficiente em termos de número de ciclos de negociação, número de argumentos trocados pelos agentes e número de acordos alcançados

Dialogue Model Using Arguments for Consensus Decision Making Through Common Knowledge Formation

Argumentation plays an important role in reasoning and allows the justification of opinions, especially when applied to collaborative decision making. Reaching consensus is not a trivial task where arguments exchanged in a dialogue and common knowledge are important for consensus. This paper presents a model of argumentative dialogue to support the formation of common knowledge in a group of agents that communicate by sending arguments, and proposes a semantics for consensus decision making. The output of the model is a weighted argumentation graph in which semantics is used to decide the preference of the group

A gradual semantics with imprecise probabilities for support argumentation frameworks

Support Argumentation Frameworks (SAFs) are a type of the Abstract Argumentation Framework, where the interactions between arguments have a positive nature. A quantitative way of evaluating the arguments in a SAF is by applying a gradual semantics, which assigns a numerical value to each argument with the aim of ranking or evaluate them. In the literature,studied gradual semantics determine precise probability values; however, in many applications there is the necessity of imprecise evaluations which consider a range of values for assessing an argument. Thus, the first contribution of this article is an imprecise gradual semantics (IGS) based on credal networks theory. The second contribution is a set of properties for evaluating IGSs, which extend some properties proposed for precise gradual semantics. Besides, we suggest a classification of semantics considering the set of properties and evaluate our proposed IGS according to the extended properties. Finally, the practical application of the results is discussed by using an example from Network Science, i.e, PageRank. We also discuss how gradual semantics benefit PageRank research by allowing to generate contrastive explanations about the scores in a more natural way

A gradual semantics with imprecise probabilities for support argumentation frameworks

Support Argumentation Frameworks (SAFs) are a type of the Abstract Argumentation Framework, where the interactions between arguments have a positive nature. A quantitative way of evaluating the arguments in a SAF is by applying a gradual semantics, which assigns a numerical value to each argument with the aim of ranking or evaluate them. In the literature,studied gradual semantics determine precise probability values; however, in many applications there is the necessity of imprecise evaluations which consider a range of values for assessing an argument. Thus, the first contribution of this article is an imprecise gradual semantics (IGS) based on credal networks theory. The second contribution is a set of properties for evaluating IGSs, which extend some properties proposed for precise gradual semantics. Besides, we suggest a classification of semantics considering the set of properties and evaluate our proposed IGS according to the extended properties. Finally, the practical application of the results is discussed by using an example from Network Science, i.e, PageRank. We also discuss how gradual semantics benefit PageRank research by allowing to generate contrastive explanations about the scores in a more natural way