A defeasible logic programming with extra meta-level information through labels

Several argument-based formalisms have emerged with application in many areas, such as legal reasoning, intelligent web search, recommender systems, autonomous agents and multi-agent systems. In decision support systems, autonomous agents need to perform epistemic and practical reasoning; the first requiring reasoning about what to believe, and the latter, involving reasoning about what to do reaching decisions and, often, attaching more information to the pieces of knowledge involved. We will introduce an approach in the framework of DeLP called Argumentative Label Algebra (ALA), incorporating labels as a medium to convey meta-level information; through these labels it will represent different features of interest in the reasoning process, such as strength and weight measures, time availability, degree of reliability, etc. The labels associated with arguments will thus be combined and propagated according to argument interactions. This information can be used for different purposes: to carry information for a specific purpose, to determine which argument defeats another, analyzing a feature that is relevant to the domain, and to define an acceptability threshold which will determine if the arguments are strong enough to be accepted. The aim of this work is to improve the ability of representing real-world scenarios in argumentative systems by modeling different arguments attributes through labels.Varios formalismos basados en argumentos han emergido, con aplicaciones en muchas áreas, tales como el razonamiento legal, la búsqueda inteligente en la web, sistemas de recomendación, agentes autónomos y sistemas multi-agente. En los sistemas de soporte a la decisión, los agentes autónomos necesitan realizar razonamiento epistémico y práctico, el primero requiere razonamiento sobre qué creer, y el ´ultimo involucra razonamiento acerca de qué hacer, frecuentemente, agregando más información a las piezas de conocimiento involucradas. Introduciremos una aproximación en el marco de DeLP denominada Álgebra de Etiqueta para Argumentos (AEA), incorporando etiquetas como un medio para transmitir información de meta-nivel. A través de estas etiquetas se puede representar diferentes rasgos de interés en el proceso de razonamiento, tales como las medidas de peso y fuerza, disponibilidad de tiempo, grados de confiabilidad, etc. Las etiquetas asociadas con los argumentos podrán así ser combinadas y propagadas de acuerdo a las interacciones de los argumentos. Esta información puede ser usada para diferentes propósitos: llevar información para un objetivo específico, determinar cuáles argumentos derrotan a otros, analizar un rasgo que es relevante a un dominio, y definir un umbral de aceptabilidad que determinar´a si un argumento es lo suficientemente fuerte como para ser aceptado. El objetivo de este trabajo es mejorar la habilidad de representar escenarios del mundo real en sistemas argumentativos al modelar diferentes atributos de los argumentos a través de las etiquetas.Fil: Budan, Maximiliano Celmo David. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina. Universidad Nacional de Santiago del Estero; ArgentinaFil: Gomez Lucero, Mauro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentin

An approach to argumentative reasoning servers with multiple preference criteria

Argumentation is a reasoning mechanism of dialectical and non-monotonic na- ture, with useful properties of computational tractability. In dynamic domains where agents deal with incomplete and contradictory information, an argument comparison criterion can be used to determine the accepted information; ar- gumentation systems with a single argument comparison criterion have been widely studied. In some of these approaches the comparison criterion is fixed, while in others a criterion can be selected and replaced in a modular way. In this work, we introduce an argumentative server that provides recommendations to its client agents and the possibility of indicating under what conditions an argument comparison criterion can be chosen to answer a particular query. To achieve this, we formalize a special type of query which, by using a conditional expression, allows the server to dynamically choose a criterion. As a result, several properties of these expressions will be studied.Fil: Teze, Juan Carlos Lionel. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Universidad Nacional del Sur. Departamento de Ciencias de la Administración; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gottifredi, Sebastián. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: García, Alejandro Javier. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Simari, Guillermo Ricardo. Universidad Nacional del Sur. Departamento de Ciencia e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Inteligencia Artificial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

An Approach to Argumentative Reasoning Servers with Multiple Preference Criteria

Argumentation is an attractive reasoning mechanism due to its dialectical and non monotonic nature, and its properties of computational tractability. In dynamic domains where the agents deal with incomplete and contradictory information, to determine the accepted or warranted information, an argument comparison criterion must be used. Argumentation systems that use a single argument comparison criterion have been widely studied in the literature. In some of these approaches, the comparison is xed and in others the criterion can be replaced in a modular way. In this work we introduce an argumentative server that provides recommendations to its client agents and the ability to decide how multiple argument comparison criteria can be combined. In the proposed formalism, the argumentative reasoning is based on the criteria selected by the client agents. As a result, a set of operators to combine multiple preference criteria is presented.Sociedad Argentina de Informática e Investigación Operativ

Weighted logics for artificial intelligence : an introductory discussion

International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas

Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential capacity of defeasible argumentation, a formalism particularly envisioned for modelling non-monotonic reasoning. In addition to this, fuzzy reasoning and expert systems, extended for handling non-monotonicity of reasoning, are selected and employed as baselines, due to their vast and accepted use within the AI community. Computational trust was selected as the domain of application of such models. Trust is an ill-defined construct, hence, reasoning applied to the inference of trust can be seen as non-monotonic. Inference models were designed to assign trust scalars to editors of the Wikipedia project. Scalars assigned to recognised trustworthy editors provided the basis for the analysis of the models’ inferential capacity according to evaluation metrics from the domain of computational trust. In particular, argument-based models demonstrated more robustness than those built upon the baselines despite the knowledge bases or datasets employed. This study contributes to the body of knowledge through the exploitation of defeasible argumentation and its comparison to similar approaches. It provides publicly implementations for the designed models of inference, which might be a useful aid to scholars interested in performing non-monotonic reasoning activities. It adds to previous works, empirically enhancing the generalisability of defeasible argumentation as a compelling approach to reason with quantitative data and uncertain knowledge

An Approach to Argumentative Reasoning Servers with Multiple Preference Criteria

Argumentation is an attractive reasoning mechanism due to its dialectical and non monotonic nature, and its properties of computational tractability. In dynamic domains where the agents deal with incomplete and contradictory information, to determine the accepted or warranted information, an argument comparison criterion must be used. Argumentation systems that use a single argument comparison criterion have been widely studied in the literature. In some of these approaches, the comparison is xed and in others the criterion can be replaced in a modular way. In this work we introduce an argumentative server that provides recommendations to its client agents and the ability to decide how multiple argument comparison criteria can be combined. In the proposed formalism, the argumentative reasoning is based on the criteria selected by the client agents. As a result, a set of operators to combine multiple preference criteria is presented.Sociedad Argentina de Informática e Investigación Operativ

Computational Complexity of Strong Admissibility for Abstract Dialectical Frameworks

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments arecalled semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexityof the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument