Operations on admissible attack scenarios

In classical abstract argumentation, arguments interact with each other through a single abstract notion of attack. However, several concrete forms of argument conflict are present in the literature, all of them of different nature and strength for a particular context. In this work we define an argumentation framework equipped with a set of abstract attack relations of varied strength. Using this framework, semantic notions dealing with the relative difference of strength are introduced. The focus is put on argument defense, and the study of admissible sets according to the quality of defenders.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

An Axiomatic Approach to Support in Argumentation

International audienceIn the context of bipolar argumentation (argumentation with two kinds of interaction, attacks and supports), we present an axiomatic approach for taking into account a special interpretation of the support relation, the necessary support. We propose constraints that should be imposed to a bipolar argumentation system using this interpretation. Some of these constraints concern the new attack relations, others concern acceptability. We extend basic Dung’s framework in different ways in order to propose frameworks suitable for encoding these constraints. By the way, we propose a formal study of properties of necessary support

Towards a Computational Analysis of Probabilistic Argumentation Frameworks

In this paper we analyze probabilistic argumentation frameworks (PAFs), defined as an extension of Dung abstract argumentation frameworks in which each argument n is asserted with a probability p(n). The debate around PAFs has so far centered on their theoretical definition and basic properties. This work contributes to their computational analysis by proposing a first recursive algorithm to compute the probability of acceptance of each argument under grounded and preferred semantics, and by studying the behavior of PAFs with respect to reinstatement, cycles and changes in argument structure. The computational tools proposed may provide strategic information for agents selecting the next step in an open argumentation process and they represent a contribution in the debate about gradualism in abstract argumentation

Operations on admissible attack scenarios

In classical abstract argumentation, arguments interact with each other through a single abstract notion of attack. However, several concrete forms of argument conflict are present in the literature, all of them of different nature and strength for a particular context. In this work we define an argumentation framework equipped with a set of abstract attack relations of varied strength. Using this framework, semantic notions dealing with the relative difference of strength are introduced. The focus is put on argument defense, and the study of admissible sets according to the quality of defenders.Presentado en el X Workshop Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Generalizations of dung frameworks and their role in formal argumentation

This article provides a short survey of some of the most popular abstract argumentation frameworks available today. The authors present the general idea of abstract argumentation, highlighting the role of abstract frameworks in the argumentation process, and review the original Dung frameworks and their semantics. A discussion of generalizations of these frameworks follows, focusing on structures taking preferences and values into account and approaches in which not only attack but also support relations can be modeled. Finally, the authors review the concept of abstract dialectical frameworks, one of the most general systems for abstract argumentation providing a flexible, principled representation of arbitrary argument relations

Examining the Modelling Capabilities of Defeasible Argumentation and non-Monotonic Fuzzy Reasoning

Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload and its assessment. Different argument-based and non-monotonic fuzzy reasoning models have been designed considering knowledge-bases of incremental complexity containing uncertain and conflicting information provided by a human reasoner. Findings showed how their inferences have a moderate convergent and face validity when compared respectively to those of an existing baseline instrument for mental workload assessment, and to a perception of mental workload self-reported by human participants. This confirmed how these models also reasonably represent the construct under consideration. Furthermore, argument-based models had on average a lower mean squared error against the self-reported perception of mental workload when compared to fuzzy-reasoning models and the baseline instrument. The contribution of this research is to provide scholars, interested in formalisms on knowledge-representation and non-monotonic reasoning, with a novel approach for empirically comparing their inferential capacity

Formalising Human Mental Workload as a Defeasible Computational Concept

Human mental workload has gained importance, in the last few decades, as a fundamental design concept in human-computer interaction. It can be intuitively defined as the amount of mental work necessary for a person to complete a task over a given period of time. For people interacting with interfaces, computers and technological devices in general, the construct plays an important role. At a low level, while processing information, often people feel annoyed and frustrated; at higher level, mental workload is critical and dangerous as it leads to confusion, it decreases the performance of information processing and it increases the chances of errors and mistakes. It is extensively documented that either mental overload or underload negatively affect performance. Hence, designers and practitioners who are ultimately interested in system or human performance need answers about operator workload at all stages of system design and operation. At an early system design phase, designers require some explicit model to predict the mental workload imposed by their technologies on end-users so that alternative system designs can be evaluated. However, human mental workload is a multifaceted and complex construct mainly applied in cognitive sciences. A plethora of ad-hoc definitions can be found in the literature. Generally, it is not an elementary property, rather it emerges from the interaction between the requirements of a task, the circumstances under which it is performed and the skills, behaviours and perceptions of the operator. Although measuring mental workload has advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. Many researchers agree that too many ad-hoc models are present in the literature and that they are applied subjectively by mental workload designers thereby limiting their application in different contexts and making comparison across different models difficult. This thesis introduces a novel computational framework for representing and assessing human mental workload based on defeasible reasoning. The starting point is the investigation of the nature of human mental workload that appears to be a defeasible phenomenon. A defeasible concept is a concept built upon a set of arguments that can be defeated by adding additional arguments. The word ‘defeasible’ is inherited from defeasible reasoning, a form of reasoning built upon reasons that can be defeated. It is also known as non-monotonic reasoning because of the technical property (non-monotonicity) of the logical formalisms that are aimed at modelling defeasible reasoning activity. Here, a conclusion or claim, derived from the application of previous knowledge, can be retracted in the light of new evidence. Formally, state-of-the-art defeasible reasoning models are implemented employing argumentation theory, a multi-disciplinary paradigm that incorporates elements of philosophy, psychology and sociology. It systematically studies how arguments can be built, sustained or discarded in a reasoning process, and it investigates the validity of their conclusions. Since mental workload can be seen as a defeasible phenomenon, formal defeasible argumentation theory may have a positive impact in its representation and assessment. Mental workload can be captured, analysed, and measured in ways that increase its understanding allowing its use for practical activities. The research question investigated here is whether defeasible argumentation theory can enhance the representation of the construct of mental workload and improve the quality of its assessment in the field of human-computer interaction. In order to answer this question, recurrent knowledge and evidence employed in state-of-the-art mental workload measurement techniques have been reviewed in the first place as well as their defeasible and non-monotonic properties. Secondly, an investigation of the state-of-the-art computational techniques for implementing defeasible reasoning has been carried out. This allowed the design of a modular framework for mental workload representation and assessment. The proposed solution has been evaluated by comparing the properties of sensitivity, diagnosticity and validity of the assessments produced by two instances of the framework against the ones produced by two well known subjective mental workload assessments techniques (the Nasa Task Load Index and the Workload Profile) in the context of human-web interaction. In detail, through an empirical user study, it has been firstly demonstrated how these two state-of-the-art techniques can be translated into two particular instances of the framework while still maintaining the same validity. In other words, the indexes of mental workload inferred by the two original instruments, and the ones generated by their corresponding translations (instances of the framework) showed a positive and nearly perfect statistical correlation. Additionally, a new defeasible instance built with the framework showed a better sensitivity and a higher diagnosticity capacity than the two selected state-of-the art techniques. The former showed a higher convergent validity with the latter techniques, but a better concurrent validity with performance measures. The new defeasible instance generated indexes of mental workload that better correlated with the objective time for task completion compared to the two selected instruments. These findings support the research question thereby demonstrating how defeasible argumentation theory can be successfully adopted to support the representation of mental workload and to enhance the quality of its assessments. The main contribution of this thesis is the presentation of a methodology, developed as a formal modular framework, to represent mental workload as a defeasible computational concept and to assess it as a numerical usable index. This research contributes to the body of knowledge by providing a modular framework built upon defeasible reasoning and formalised through argumentation theory in which workload can be optimally measured, analysed, explained and applied in different contexts