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

Decision Support for Agri-Food Chains: A Reverse Engineering Argumentation-Based Approach

UMR IATE Axe 5 : Application intégrée de la connaissance, de l’information et des technologies permettant d’accroître la qualité et la sécurité des alimentsInternational audienceEvaluating food quality is a complex process since it relies on numerous criteria historically grouped into four main types: nutritional, sensorial, practical and hygienic qualities. They may be completed by other emerging preoccupations such as the environmental impact, economic phenomena, etc. However, all these aspects of quality and their various components are not always compatible and their simultaneous improvement is a problem that sometimes has no obvious solution, which corresponds to a real issue for decision making. This paper proposes a decision support method guided by the objectives defined for the end products of an agrifood chain. It is materialised by a backward chaining approach based on argumentation

Using Argumentation in a French Agrifood Chain Application: Technical Report

Evaluating food quality is a complex process since it relies on nu- merous criteria historically grouped into four main types: nutritional, sensorial, practical and hygienic qualities. They may be completed by other emerging preoccupations such as the environmental impact, eco- nomic phenomena, etc. However, all these aspects of quality and their various components are not always compatible and their simultaneous improvement is a problem that sometimes has no obvious solution, which corresponds to a real issue for decision making. This paper proposes a decision support method guided by the objectives de ned for the end products of an agrifood chain. It is materialized by a backward chaining approach based on argumentation

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

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

Default Conceptual Graph Rules: Preliminary Results for an Agronomy Application

International audienceIn this paper, we extend Simple Conceptual Graphs with Reiter's default rules. The motivation for this extension came from the type of reasonings involved in an agronomy application, namely the simulation of food processing. Our contribution is many fold: rst, the expressivity of this new language corresponds to our modeling purposes. Second, we provide an effective characterization of sound and complete reasonings in this language. Third, we identify a decidable subclass of Reiter's default logics. Last we identify our language as a superset of SREC-, and provide the lacking semantics for the latter language

An iterative approach to build relevant ontology-aware data-driven models

knowledge integrationInternational audienceIn many fields involving complex environments or living organisms, data-driven models are useful to make simulations in order to extrapolate costly experiments and to design decision-support tools. Learning methods can be used to build interpretable models from data. However, to be really useful, such models must be trusted by their users. From this perspective, the domain expert knowledge can be collected and modelled to help guiding the learning process and to increase the confidence in the resulting models, as well as their relevance. Another issue is to design relevant ontologies to formalize complex knowledge. Interpretable predictive models can help in this matter. In this paper, we propose a generic iterative approach to design ontology-aware and relevant data-driven models. It is based upon an ontology to model the domain knowledge and a learning method to build the interpretable models (decision trees in this paper). Subjective and objective evaluations are both involved in the process. A case study in the domain of Food Industry demonstrates the interest of this approach

A Generic Software to Support Collective Decision in Food Chains and in Multi-Stakeholder Situations

International audienceMyChoice is a user-friendly web-based application supporting collective decision, developed by INRAE (French National Institute of Research for Agriculture, Food and the Environment). It is designed to analyse, compare and assess the acceptability of different alternatives-e.g. technologies, food processes, variants of a product, etc.-, based on explicative arguments stemming from various sources and stakeholders, regarding different criteria and aims. It is well-suited for accompanying news trends and developments in food chains, requiring the adhesion and cooperation of various stakeholders. Nevertheless, its design is generic and may also be applied to different fields. This paper presents the design concepts of the software, stemming from different disciplines-multicriteria decision, AI argumentation, database information systems, social psychology-, its features and expected future developments

Supporting Argumentation Systems by Graph Representation and Computation

International audienceArgumentation is a reasoning model based on arguments and on attacks between arguments. It consists in evaluating the acceptability of arguments, according to a given semantics. Due to its generality, Dung's framework for abstract argumentation systems, proposed in 1995, is a reference in the domain. Argumentation systems are commonly represented by graph structures, where nodes and edges respectively represent arguments and attacks between arguments. However beyond this graphical support, graph operations have not been considered as reasoning tools in argumentation systems. This paper proposes a conceptual graph representation of an argumentation system and a computation of argument acceptability relying on conceptual graph default rules

Coupling agent-based models and argumentation framework to simulate opinion dynamics: application to vegetarian diet diffusion

International audienceAgent-based simulation has been extensively used to studyopinion dynamics. However, the vast majority of the existing modelshave been limited to extremely abstract and simplified representationsof the diffusion process, which impairs the realism of the simulationsand disables the understanding of the reasons for the shift of an actor’sopinion. This paper presents a generic framework implemented in theGAMA platform allowing to explicitly represent exchanges of argumentsbetween actors in a context of an opinion dynamic model. More precisely,we propose to formalize the inner attitude towards an opinion of eachagent as an argumentation graph and give them the possibility to sharearguments with other agents. We present an application of the frameworkto study the evolution of the vegetarian diet at a city level