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

Towards an Argument Interchange Format for Multi-Agent Systems

Abstract. This document describes a strawman specification for an Argument Interchange Format (AIF) that might be used for data exchange between Argumentation tools or communication in Multi-Agent Systems (MAS). The document started life as a skeleton for contributions from participants in the Technical Forum Group meeting in Budapest in September 2005, receiving also input from third parties. The results were subsequentely improved and added to by online discussion to form a more substantial. In its current form, this document is intended to be a strawman model which serves as a point of discussion for the community rather than an attempt at a definitive, all encompassing model. The hope is that it could provide a useful input to ArgMAS discussion in paricular on the utility of common Argumentation Interchange Formats, what form they might take and a potential research / development agenda to help realise them.

Making Argument Systems Computationally Attractive - Argument Construction and Maintenance

Argumentative systems (Pollock, 1987; Vreeswijk, 1989; Prakken, 1993) are formalizations of the process..

What Can Argumentation Do for Inconsistent Ontology Query Answering?

The area of inconsistent ontological knowledge base query answering studies the problem of inferring from an inconsistent ontology. To deal with such a situation, different semantics have been defined in the literature (e.g. AR, IAR, ICR). Argumentation theory can also be used to draw conclusions under inconsistency. Given a set of arguments and attacks between them, one applies a particular semantics (e.g. stable, preferred, grounded) to calculate the sets of accepted arguments and conclusions. However, it is not clear what are the similarities and differences of semantics from ontological knowledge base query answering and semantics from argumentation theory. This paper provides the answer to that question. Namely, we prove that: (1) sceptical acceptance under stable and preferred semantics corresponds to ICR semantics; (2) universal acceptance under stable and preferred semantics corresponds to AR semantics; (3) acceptance under grounded semantics corresponds to IAR semantics. We also prove that the argumentation framework we define satisfies the rationality postulates (e.g. consistency, closure)

Making decisions from weighted arguments

International audienceHumans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various strengths. In spite of the usefulness of arguments in a decision making process, there have been few formal proposals handling this idea if we except works by Fox and Parsons and by Bonet and Geffner. In this paper we propose a possibilistic logic framework where arguments are built from a knowledge base with uncertain elements and a set of prioritized goals. The proposed approach can compute two kinds of decisions by distinguishing between pessimistic and optimistic attitudes. When the available, maybe uncertain, knowledge is consistent, as well as the set of prioritized goals (which have to be fulfilled as far as possible), the method for evaluating decisions on the basis of arguments agrees with the possibility theory-based approach to decision-making under uncertainty. Taking advantage of its relation with formal approaches to defeasible argumentation, the proposed framework can be generalized in case of partially inconsistent knowledge, or goal bases

P.Y.: A more expressive softgoal conceptualization for quality requirements analysis

Abstract. Initial software quality requirements tend to be imprecise, subjective, idealistic, and context-specific. An extended characterization of the common Softgoal concept is proposed for representing and reasoning about such requirements during the early stages of the requirements engineering process. The types of information often implicitly contained in a Softgoal instance are highlighted to allow richer requirements to be obtained. On the basis of the revisited conceptual foundations, guidelines are suggested as to the techniques that need to be present in requirements modeling approaches that aim to employ the given Softgoal conceptualization. 1 Dealing with Software Quality Requirements Ensuring the quality of software has become a major issue in software engineering research and practice since the 1970s [5]. As increasingly complex software plays a critical role in business, comprehensive and precise methods and tools are needed to create software products and services that are safe, dependable, and efficient [26]. Software quality is defined by the International Organization for Standardization [12] as the totality of features and characteristics of a software product that bear on its ability to satisfy stated or implied needs. Ensuring the quality of software therefore amounts to making sure that software behavior is in line with stated and implied needs. It is widely acknowledged that quality needs to be taken into account early in the software development process [8,30,19]. Quality requires specifying stated and implied needs. Approaches focusing on ensuring quality during the development process by guiding functional requirements specification decisions by quality considerations, so that the latter justify the former, are termed processoriented. In contrast, product-oriented approaches (e.g., [11,13]) evaluate the quality of already developed software products, and are particularly relevant for, e.g., component selection [2]

Approaches to measuring inconsistent information

Abstract. Measures of quantity of information have been studied extensively for more than fifty years. The seminal work on information theory is by Shannon [67]. This work, based on probability theory, can be used in a logical setting when the worlds are the possible events. This work is also the basis of Lozinskii’s work [48] for defining the quantity of information of a formula (or knowledgebase) in propositional logic. But this definition is not suitable when the knowledgebase is inconsistent. In this case, it has no classical model, so we have no “event ” to count. This is a shortcoming since in practical applications (e.g. databases) it often happens that the knowledgebase is not consistent. And it is definitely not true that all inconsistent knowledgebases contain the same (null) amount of information, as given by the “classical information theory”. As explored for several years in the paraconsistent logic community, two inconsistent knowledgebases can lead to very different conclusions, showing that they do not convey the same information. There has been som