A Framework for Combining Defeasible Argumentation with Labeled Deduction

In the last years, there has been an increasing demand of a variety of logical systems, prompted mostly by applications of logic in AI and other related areas. Labeled Deductive Systems (LDS) were developed as a flexible methodology to formalize such a kind of complex logical systems. Defeasible argumentation has proven to be a successful approach to formalizing commonsense reasoning, encompassing many other alternative formalisms for defeasible reasoning. Argument-based frameworks share some common notions (such as the concept of argument, defeater, etc.) along with a number of particular features which make it difficult to compare them with each other from a logical viewpoint. This paper introduces LDSar, a LDS for defeasible argumentation in which many important issues concerning defeasible argumentation are captured within a unified logical framework. We also discuss some logical properties and extensions that emerge from the proposed framework.Comment: 15 pages, presented at CMSRA Workshop 2003. Buenos Aires, Argentin

A taxonomy for argumentative frameworks based on labelled deduction

Artificial Intelligence has long dealt with the issue of finding a suitable formalization for reasoning with incomplete and potentially inconsistent information. Defeasible argumentation [SL92,CML00,PraVre99] has proven to be a successful approach in many respects, since it naturally resembles many aspects of commonsense reasoning (see [CML00,PraVre99] for details). Besides, recent work [PraVre99,BDKT97] has shown that defeasible argumentation constitutes a confluence point for characterizing many different approaches to non-monotonic reasoning. Nevertheless, the evolution of different, alternative formalisms for modeling argumentation has resulted in a number of models that share some common features (the notion of argument, attack between arguments, defeat, dialectical analysis, etc.). This constitutes a motivation for the definition of a unified ontology, under which these different features can be analyzed and inter-related. As a byproduct from such an ontology, a classification (or taxonomy) of argumentation frameworks in terms of knowledge encoding capabilities, expressive power, etc. would be possible. In [Che01] a logical framework for defeasible argumentation called SDEAR was developed. The SDEAR framework is based on labelled deductive systems [Gab96]. Labelled Deductive Systems offer an attractive approach to formalizing complex logical systems, since they allow to characterize the different components involved by using different sorts of labels. One of the motivations for developing this framework was namely the definition of a single, unified ontology to capture the main issues involved in defeasible argumentation by specifying a suitable underlying logical language and its associated inference rules. In this presentation we focus on a particular research line which emerged from the above formalization, namely the classification of different defeasible argumentation frameworks based on features that can be ‘abstracted away’ in the SDEAR framework. The presentation is structured as follows: first, in section 2, we will briefly sketch how the SDEAR framework works. Then, in section 3 we will describe how different argumentation frameworks can be interrelated through SDEAR. Finally, section 4 concludes.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

On the use of contexts for representing knowledge in defeasible argumentation

The notion of context and its importance in knowledge representation and nonmonotonic reasoning was first discussed in Artificial Intelligence by John McCarthy. Ever since, contexts have found many applications in developing knowledge-based reasoning systems. Defeasible argumentation has gained wide acceptance within the Al community in the last years. Different argument-based frameworks have been proposed. In this respect, MTDR (Simari & Loui, 1992) has come to be one of the most successful. However, even though the formalism is theoretically sound, there exist sorne dialectical considerations involving argument construction and the inference mechanism, which impose a rather procedural approach, tightly interlocked with the system's logic. This paper discusses different uses of contexts for modelling the process of defeasible argumentation. We present an alternative view of MTDR using contexts. Our approach will allow us to discuss novel issues in MTDR, such as defining a set of moves and introducting an arbiter for regulating inference. As a result, protocols for argument generation as well as some technical considerations for speeding up inference will be kept apart from the logical machinery underlying MTDR.Eje: 2do. Workshop sobre aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Formalizing processes in defeasible argumentation using labeled deductive systems

This paper summarizes the main results developed in the author's PhD Thesis. The main goal of the Thesis is to provide a formalization of defeasible argumentation oriented towards its computational treatment. In order to do this, an LDS-based logical framework for defeasible argumentation called LDSar has been developed. The object language is that of logic programming, complemented with labels that identify distinguished elements for representing knowledge and performing inference.Resumen de la tesis doctoral presentada por el autor en la Universidad del Sur.Facultad de Informátic

Formalizing argument-based agent interaction in electronic institutions

During the last decade the notion of agent has gained acceptance within the AI community, mainly due to its adequacy to formalize complex environments. Agents can be thought as active software objects, which may be autonomous and able to perceive, reason, act, and interact with other agents. When agents interact with each other, a multi-agent system (MAS) arises.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

Using logic programs to model an agent's epistemic state

The notion of rational agency was proposed by Russell [9] as an alternative characterization of intelligence agency. Loosely speaking, an agent is said to be rational if it perfomns the right actions according to the information it possesses and the goals it wants to achieve. Unfortunately, the enterprise of constructing a rational agent is a rather complex task. Although in the last few years there has been an intense flowering of interest in the subject, it is still in its early beginnings: several issues remain overlooked or addressed under too unrealistic assumptions. As slated by Pollock. [5], a rational agent should have models of itself and its surroundings, since it must be able to draw conclusions from this knowledge that compose its set of beliefs. Traditional approaches rely on multi-modal logics to represent the agent's epistemic state [7. l]. Given the expressive power of these formalisms, their use yields proper theoretical models. Nevertheless, the advantages of these specifications lend to be lost in the transition towards practical systems: there is a tenuous relation between the implementations based on these logics and their theoretical foundations [8]. Modal logics systems suffer from a number of drawbacks, notably the well-known logical omniscience problem [10]. This problem arises as a by-product of the necessitation rule and the K axiom, present in any normal modal system. Together, these ruIes imply two unrealistic conditions: an agent using this system must know all the valid formulas, and its beliefs should be closed under logical consecuence. These properties are overstrong for a resource-bounded reasoner lo achieve them. Therefore, the traaditional modal logic approach is not suitable for representing practical believers [11]. We intend to use logic programs as an alternative representation for the agent's epistemic state. This formalization avoids the aforementioned problems of modal logics, and admits a seamless transition between theory and practice. In the next section we detail our model and highlight its advantages. Next, sectiol1 3 prescnts sume conclusions and reports on the forthcoming work.Eje: Aspectos teóricos de inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Introducing dialectical bases in defeasible argumentation

Defeasible argumentation is a form of defeasible reasoning, that emphasizes the notion of an argument. An argument A for a conclusion q is a tentative piece of reasoning which supports q. In an argumentative framework, common sense reasoning can be modeled as a process in which we must determine whether an argument justifies its conclusion. The process mentioned aboye takes considerable computational effort. For this reason it would be convenient to keep a repository of already computed justifications to save work already done with previously solved queries. In this paper we introduce the concept of dialectical bases as a first step in direction to defining a justification maintenance system for argumentative frameworks.Eje: Aspectos teóricos de la inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

A taxonomy for argumentative frameworks based on labelled deduction

Artificial Intelligence has long dealt with the issue of finding a suitable formalization for reasoning with incomplete and potentially inconsistent information. Defeasible argumentation [SL92,CML00,PraVre99] has proven to be a successful approach in many respects, since it naturally resembles many aspects of commonsense reasoning (see [CML00,PraVre99] for details). Besides, recent work [PraVre99,BDKT97] has shown that defeasible argumentation constitutes a confluence point for characterizing many different approaches to non-monotonic reasoning. Nevertheless, the evolution of different, alternative formalisms for modeling argumentation has resulted in a number of models that share some common features (the notion of argument, attack between arguments, defeat, dialectical analysis, etc.). This constitutes a motivation for the definition of a unified ontology, under which these different features can be analyzed and inter-related. As a byproduct from such an ontology, a classification (or taxonomy) of argumentation frameworks in terms of knowledge encoding capabilities, expressive power, etc. would be possible. In [Che01] a logical framework for defeasible argumentation called SDEAR was developed. The SDEAR framework is based on labelled deductive systems [Gab96]. Labelled Deductive Systems offer an attractive approach to formalizing complex logical systems, since they allow to characterize the different components involved by using different sorts of labels. One of the motivations for developing this framework was namely the definition of a single, unified ontology to capture the main issues involved in defeasible argumentation by specifying a suitable underlying logical language and its associated inference rules. In this presentation we focus on a particular research line which emerged from the above formalization, namely the classification of different defeasible argumentation frameworks based on features that can be ‘abstracted away’ in the SDEAR framework. The presentation is structured as follows: first, in section 2, we will briefly sketch how the SDEAR framework works. Then, in section 3 we will describe how different argumentation frameworks can be interrelated through SDEAR. Finally, section 4 concludes.Eje: Inteligencia Artificial Distribuida, Aspectos Teóricos de la Inteligencia Artificial y Teoría de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

Characterizing defeat in observation-based defeasible logic programming

In this work we analyze the problem of incorporating specificity to characterize defeat in a particular argumentative framework, called observation based defeasible logic programming (ODeLP) [1]. Eficiency is an important issues in ODeLP, since this framework has been de ned for representing the knowledge of intelligent agents in real world applications. Computing specificity using domain knowledge is a demanding operation. Thus, have devised a new version of this criterion, that optimizes the computation of the defeat relation.Eje: Inteligencia artificial distribuida, aspectos teóricos de la inteligencia artificial y teoría de computaciónRed de Universidades con Carreras en Informática (RedUNCI