Argumentation-based fault diagnosis for home networks

Home networks are a fast growing market but managing them is a difficult task, and diagnosing faults is even more challenging. Current fault management tools provide comprehensive information about the network and the devices but it is left to the user to interpret and reason about the data and experiment in order to find the cause of a problem. Home users may not have motivation or time to learn the required skills. Furthermore current tools adopt a closed approach which hardcodes a knowledge base, making them hard to update and extend. This paper proposes an open fault management framework for home networks, whose goal is to simplify network troubleshooting for non-expert users. The framework is based on assumption-based argumentation that is an AI technique for knowledge representation and reasoning. With the underlying argumentation theory, we can easily capture and model the diagnosis procedures of network administrators. The framework is rule-based and extensible, allowing new rules to be added into the knowledge base and diagnostic strategies to be updated on the fly.The framework can also utilise external knowledge and make distributed diagnosi

Theory of Regulatory Compliance for Requirements Engineering

Regulatory compliance is increasingly being addressed in the practice of requirements engineering as a main stream concern. This paper points out a gap in the theoretical foundations of regulatory compliance, and presents a theory that states (i) what it means for requirements to be compliant, (ii) the compliance problem, i.e., the problem that the engineer should resolve in order to verify whether requirements are compliant, and (iii) testable hypotheses (predictions) about how compliance of requirements is verified. The theory is instantiated by presenting a requirements engineering framework that implements its principles, and is exemplified on a real-world case study.Comment: 16 page

An Artificial Intelligence-Based Approach for Arbitration in Food Chains

International audienceFood chain analysis is a highly complex procedure since it relies on numerous criteria of various types: environmental, economical, functional, sanitary, etc. Quality objectives imply different stakeholders, technicians, managers, professional organizations, end-users, public collectivities, etc. Since the goals of the implied stakeholders may be divergent, decision-making raises arbitration issues. Arbitration can be done through a compromise - a solution that satisfies, at least partially, all the actors - or favor some of the actors, depending on the decision-maker's priorities. Several questions are open to support arbitration in food chains: what kind of representation and reasoning model is suitable to allow for contradictory viewpoints ? How can stakeholders' divergent priorities be taken into account ? How can the conflicts be solved to achieve a tradeoff within a decision-support system ? This paper proposes an artificial intelligence-based approach to formalize available knowledge as elements for decision-making. It develops an argumentation-based approach to support decision in food chains and presents an analysis of a case study concerning risks/benefits within the wheat to bread chain. It concerns the controversy about the possible change in the ash content of the flour used for commonly consumed French bread, and implies several stakeholders of the chain

Associer argumentation et simulation en aide à la décision : Illustration en agroalimentaire

International audiencePrendre une décision impliquant plusieurs acteurs aux objectifs diver-gents nécessite de considérer des informations tant qualitatives – les préférences des acteurs sur les décisions possibles – que quantitatives – les paramètres servant d'indicateurs pour les acteurs. Dans cet article nous nous intéressons à l'as-sociation de ces deux types d'approches. Le modèle qualitatif considéré est l'ar-gumentation. Le modèle quantitatif simulant les scénarios découlant de chaque décision est la dynamique des systèmes. Cet article s'intéresse aux éléments per-mettant de connecter les deux formalismes. Un exemple en agroalimentaire vient en appui à cette réflexion

Fusion de données redondantes : une approche explicative

National audienceNous nous intéressons, dans le cadre du projet ANR Qualinca au trai-tement des données redondantes. Nous supposons dans cet article que cette re-dondance a déjà été établie par une étape préalable de liage de données. La question abordée est la suivante : comment proposer une représentation unique en fusionnant les "duplicats" identifiés ? Plus spécifiquement, comment décider, pour chaque propriété de la donnée considérée, quelle valeur choisir parmi celles figurant dans les "duplicats" à fusionner ? Quelle méthode adopter dans le but de pouvoir, par la suite, retracer et expliquer le résultat obtenu de façon trans-parente et compréhensible par l'utilisateur ? Nous nous appuyons pour cela sur une approche de décision multicritère et d'argumentation

Counting Complexity for Reasoning in Abstract Argumentation

In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.Comment: Extended version of a paper published at AAAI-1

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering