María Paula González

The Added Value of Argumentation

We discuss the value of argumentation in reaching agreements, based on its capability for dealing with conflicts and uncertainty. Logic-based models of argumentation have recently emerged as a key topic within Artificial Intelligence. Key reasons for the success of these models is that they are akin to human models of reasoning and debate, and their generalisation to frameworks for modelling dialogues. They therefore have the potential for bridging between human and machine reasoning in the presence of uncertainty and conflict. We provide an overview of a number of examples that bear witness to this potential, and that illustrate the added value of argumentation. These examples amount to methods and techniques for argumentation to aid machine reasoning (e.g. in the form of machine learning and belief functions) on the one hand and methods and techniques for argumentation to aid human reasoning (e.g. for various forms of decision making and deliberation and for the Web) on the other. We also identify a number of open challenges if this potential is to be realised, and in particular the need for benchmark libraries.

A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues

Automating customer complaints processing is a major issue in the context of knowledge management technologies for most companies nowadays. Automated decision-support systems are important for complaint processing, integrating human experience in understanding complaints and the application of machine learning techniques. In this context, a major challenge in complaint processing involves assessing the validity of a customer complaint on the basis of the emerging dialogue between a customer and a company representative. This paper presents a novel approach for modelling and classifying complaint scenarios associated with customer-company dialogues. Such dialogues are formalized as labelled graphs, in which both company and customer interact through communicative actions, providing arguments that support their points. We show that such argumentation provides a complement to perform machine learning reasoning on communicative actions, improving the resulting classification accuracy.

Empowering Recommendation Technologies Through Argumentation

User support systems have evolved in the last years as specialized tools to assist users in a plethora of computer-mediated tasks by providing guidelines or hints [19]. Recommender systems are a special class of user support tools that act in cooperation with users, complementing their abilities and augmenting their performance by offering proactive or on-demand, context-sensitive support. Recommender systems are mostly based on machine learning and information retrieval algorithms, providing typically suggestions based on quantitative evidence (i.e. measures of similarity between objects or users). The inference process which led to such suggestions is mostly unknown (i.e. ‘black-box’ metaphor). Although the effectiveness of existing recommenders is remarkable, they still have some serious limitations. On the one hand, they are incapable of dealing formally with the defeasible nature of users’ preferences in complex environments. Decisions about user preferences are mostly based on heuristics which rely on ranking previous user choices or gathering information from other users with similar interests. On the other hand, they are not equipped with explicit inference capabilities. This is a hindrance for providing explanation facilities which could help the user to assess the analysis underlying the

Didactic strategies for promoting significant learning in formal languages and automata theory

An undergraduate course in Formal Languages and Automata Theory (FLAT) involves different topics that are core to the CS curricula and whose level of abstraction makes them difficult both to teach and to learn. Such difficulty stems from the complexity of the abstract notions involved and the required mathematical background. Surveys conducted among our students showed that many of them were applying some theoretical concepts mechanically rather than developing a significant learning of them, leading to a lack of motivation and interest. To cope with this problem, we introduced a number of didactic strategies based on a constructivist approach. The main aim of the proposed strategies is to promote a more significant learning of several important FLAT topics.

Modelling shared knowledge and shared knowledge awareness in CSCL scenarios through automated argumentation systems

Over the last few years, argumentation systems have been gaining increasing importance in several areas of Artificial Intelligence, mainly as a vehicle for facilitating rationally justifiable decision making when handling incomplete and potentially inconsistent information. Argumentation provides a sound model for dialectical reasoning, which underlies discussions among students when solving tasks collaboratively in a CSCL environment. In this setting, we identify the problem of constructing Shared Knowledge and its related Shared Knowledge Awareness. While Shared Knowledge refers to the common knowledge students acquire when they work in a collaborative activity, Shared Knowledge Awareness is associated with the consciousness on the Shared Knowledge that a particular student has. This paper presents a novel approach to model Shared Knowledge construction and the associated Shared Knowledge Awareness through an automated argumentation system.

Assessing usability problems in Latin-American academic webpages with cognitive walkthroughs and datamining techniques

Qualitative usability evaluation is usually included within the Evaluation Stage in Usability Engineering through a Qualitative Usability Testing process QUT. This QUT process includes the application of methods that have been defined focusing on the evaluation of a particular interactive system, becoming highly expensive when a context of use has to be evaluated (by analyzing a large number of interfaces belonging to that context) in order to detect common usability problems from a qualitative viewpoint. This paper presents the QUTCKDD methodology which incorporate techniques from Knowledge Discovery in Databases, extending the existing QUT process in order to solve the above situation. To illustrate the QUTCKDD methodology, an experimentation related to a particular Latin-American context of use is also discussed.

A hybrid approach for modelling early prototype evaluation under user-centred design through association rules

One of the main activities in User Centred Design (UCD) is prototype evaluation, which is traditionally performed by means of an Evaluation Stage that looks for the redefinition of the prototype requirements, involving quantitative and qualitative usability testing techniques. This paper describes a new approach in which the traditional methodology for performing the Evaluation Stage under UCD is embedded in a framework with capabilities for mining association rules. This allows to minimise the impact of the interpretation bias of the evaluation team when analysing ambiguous user statements in natural language.

Modeling User’s Sentiment in User Segmentations: An Argumentation Approach for User Centered Design

User segmentation is a practice of clustering an audience based on mutually exclusive subsets of individuals that are similar in specific ways. Nowadays user segmentation is crucial not only for the industry but also for the field of User Centered Design, where achieving an accurate understanding of the user’s behavior in the current e-scenario is becoming a complex task. The segmentation could be based on demographic issues, social-economical features, psychographic data, physical characteristics and psychological profiles, etc. This paper proposes a novel strategy for the automatic detection of critical segmentation factors that divide users focused on their feelings and opinions towards a particular topic. Given a topic and on the basis of user’s text-based opinions posted at Web 2.0 services (such as social networks, microblogging platforms, online review systems, online news media, etc.), our proposal introduces an argument-oriented methodology that integrates argumentation theory, sentiment analysis and opinion mining including the computational treatment of incomplete, contradictory or potentially inconsistent information. The mining process is characterized in terms of dialectical analysis of opinions (atomic or more complex opinions constructed by an aggregation mechanism) according to a preference criterion given by topic and feature specificity. As a result, an “opinion analysis tree” rooted in the first original topic is automatically constructed and visualized, in which any node models a user segmentation, showing the factor that define the segmentation as well as the particularities that group the subset. This way, traditional problems associated with the subjective interpretation of user’s opinions expressed in natural language are minimized. Besides, instead of defining a user’s statistical sample, all available information is considered and possible, not evident critical segmentation factors could be discovered, thus enhancing a rational decision making process.

Guidelines to develop emotional awareness devices from a cultural-perspective: a latin american example

Interpersonal communication involves more than just words; it involves emotional issues that can be roughly seen as complex organized internal states. Awareness of those states allows human beings to evaluate social information and develop strategic social intelligence. In this setting, developing emotional awareness devices can be successfully achieved under a Cultural Centred Design perspective, as social and cultural features are crucial to ensure an adequate level of emotional awareness. However, cultural-oriented recommendations are not always included to lead the promoting of an adequate emotional awareness in digital and physical devices. To cope with this problem, this paper presents a minimal set of cultural guidelines that should be taken into account to develop emotional awareness devices under Cultural Centred Design. To illustrate the proposal, the development of an extended virtual portrait is discussed by highlighting a cultural viewpoint form a Latin-American perspective.

Guidelines and usability principles to design and test shared-knowledge awareness for a CSCL interface

Shared Knowledge Awareness is defined as the consciousness on the shared knowledge that a particular student has when carrying out a collaborative learning activity in a CSCL environment. In fact, an adequate level of Shared Knowedge Awareness can be promoted by including in the interface of this environment some specific features that improve the student perception related to such knowedge. This paper proposes some basic design guidelines that should be taken into account when designing a CSCL interface in order to promote an adecuate users behavior with respect to his/her Shared Knowledge Awareness. Besides, a set of usability principles is identified and linked to every suggested guideline to evaluate its quality (in terms of “easiness to use and learnability”) in an actual interface. Two different experiments are included as real-life examples that are analized within the proposed approach.