Nicolás D. Rotstein

An argumentative reasoning service for deliberative agents

In this paper we propose a model that allows agents to deliberate using defeasible argumentation, to share knowledge with other agents, and to represent individual knowledge privately. We describe the design and implementation of a Defeasible Logic Programming Server that handles queries from several remote client agents. Queries will be answered using public knowledge stored in the Server and individual knowledge that client agents can send as part of a query, providing a particular context for it. The Server will answer these contextual queries using a defeasible argumentative analysis. Different types of contextual queries are presented and analyzed.

Formalizing dialectical explanation support for argument-based reasoning in knowledge-based systems

The concept of explanation has received attention from different areas in Computer Science, particularly in the knowledge-based systems and expert systems communities. At the same time, argumentation has evolved as a new paradigm for conceptualizing commonsense reasoning, resulting in the formalization of different argumentation frameworks and the development of several real-world argument-based applications. Although the notions of explanation and argument for a claim share many common elements in knowledge-based systems their interrelationships have not yet been formally studied in the context of the current argumentation research in Artificial Intelligence. This article explores these ideas by providing a new perspective on how to formalize dialectical explanation support for argument-based reasoning. To do this, we propose a formalization of explanations for abstract argumentation frameworks with dialectical constraints where different emerging properties are studied and analyzed. As a concrete example of the formalism introduced we show how it can be fleshed out in an implemented rule-based argumentation system.

Dialectical Explanations in Defeasible Argumentation

This work addresses the problem of providing explanation capabilities to an argumentation system. Explanation in defeasible argumentation is an important, and yet undeveloped field in the area. Therefore, we move in this direction by defining a concrete argument system with explanation facilities. We consider the structures that provide information on the warrant status of a literal. Our focus is put on argumentation systems based on a dialectical proof procedure, therefore we study dialectical explanations. Although arguments represent a form of explanation for a literal, we study the complete set of dialectical trees that justifies the warrant status of a literal, since this set has proved to be a useful tool to comprehend, analyze, develop, and debug argumentation systems.

Dynamics of Knowledge in DeLP through Argument Theory Change

This article is devoted to the study of methods to change defeasible logic programs (de.l.p.s) which are the knowledge bases used by the Defeasible Logic Programming (DeLP) interpreter. DeLP is an argumentation formalism that allows to reason over potentially inconsistent de.l.p.s. Argument Theory Change (ATC) studies certain aspects of belief revision in order to make them suitable for abstract argumentation systems. In this article, abstract arguments are rendered concrete by using the particular rule-based defeasible logic adopted by DeLP. The objective of our proposal is to define prioritized argument revision operators \`a la ATC for de.l.p.s, in such a way that the newly inserted argument ends up undefeated after the revision, thus warranting its conclusion. In order to ensure this warrant, the de.l.p. has to be changed in concordance with a minimal change principle. To this end, we discuss different minimal change criteria that could be adopted. Finally, an algorithm is presented, implementing the argument revision operations.

Generalized Abstract Argumentation: Handling Arguments in FOL Fragments

Generalized argumentation frameworks relate formulae in classical logic to arguments based on the Dungs classic framework. The main purpose of the generalization is to provide a theory capable of reasoning (following argumentation technics) about inconsistent knowledge bases (KB) expressed in FOL fragments. Consequently, the notion of argument is related to a single formula in the KB. This allows to share the same primitive elements from both, the framework (arguments) and, the KB (formulae). A framework with such features would not only allow to manage a wide range of knowledge representation languages, but also to cope with the dynamics of knowledge in a straightforward manner.

Defeasible argumentation support for an extended BDI architecture

In this work, an agent architecture that combines defeasible argumentation and the BDI model is described. Argumentation will be used as a mechanism for reasoning about beliefs, for filtering desires considering the agents current environment, and for selecting proper intentions. The approach allows to define different types of agents and this will affect the way in which desires are filtered and hence, which intention is selected. For performing defeasible reasoning, the approach uses a concrete framework based on a working defeasible argumentation system: Defeasible Logic Programming (DeLP). A set of filtering rules, represented as a defeasible logic program, will be used to represent reasons for and against adopting desires. Thus, based on its perceived or derived beliefs, the agent will argue about which of its desires are achievable in the current situation. To clarify the ideas two applications will be introduced to show two significantly different types of agent that can be implemented using this approach.

A heuristics-based pruning technique for argumentation trees

Argumentation in AI provides an inconsistency-tolerant formalism capable of establishing those pieces of knowledge that can be warranted despite having information in contradiction. Computation of warrant tends to be expensive; in order to alleviate this issue, we propose a heuristics-based pruning technique over dialectical trees. Empirical testing shows that in most cases our approach answers queries much faster than the usual techniques, which prune with no guide.

Using argument strength for building dialectical bonsai

Argumentation in AI provides an inconsistency-tolerant formalism capable of establishing those pieces of knowledge that can be accepted despite having information in contradiction. Computation of accepted arguments tends to be expensive; in order to alleviate this issue, we propose a heuristics-based pruning technique over argumentation trees. Empirical testing shows that in most cases our approach answers queries much faster than the usual techniques, which prune with no guide. The heuristics is based on a measure of strength assigned to arguments. We show how to compute these strength values by providing the corresponding algorithms, which use dynamic programming techniques to reutilise previously computed trees. In addition to this, we introduce a set of postulates characterising the desired behaviour of any strength formula. We check the given measure of strength against these postulates to show that its behaviour is rational. Although the approach presented here is based on an abstract argumentation framework, the techniques are tightly connected to the dialectical process rather than to the framework itself. Thus, results can be extrapolated to other dialectical-tree-based argumentation formalisms with no additional difficulty.

From desires to intentions through dialectical analysis

In this work, we introduce a framework where defeasible argumentation is used for reasoning about beliefs, desires and intentions. A dialectical filtering process is introduced in order to obtain a subset of the agents desires containing only those that are actually achievable in the current situation. In our framework, different agents types can be defined and this will affect the way in which current desires are obtained. Finally, intentions will be current desires that the agent may commit to pursue.