Decision support for agri-food chains: A reverse engineering argumentation-based approach

Evaluating 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.

[1]  Trevor J. M. Bench-Capon.  Persuasion in Practical Argument Using Value-based Argumentation Frameworks , 2003, J. Log. Comput..

[2]  Madalina Croitoru,et al.  What Can Argumentation Do for Inconsistent Ontology Query Answering? , 2013, SUM.

[3]  Claudette Cayrol,et al.  A Reasoning Model Based on the Production of Acceptable Arguments , 2002, Annals of Mathematics and Artificial Intelligence.

[4]  Anthony Hunter,et al.  An Argumentation-Based Approach for Decision Making , 2012, 2012 IEEE 24th International Conference on Tools with Artificial Intelligence.

[5]  B. Charnomordic,et al.  Artificial intelligence‐based decision support system to manage quality of durum wheat products , 2009 .

[6]  Diego Calvanese,et al.  Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family , 2007, Journal of Automated Reasoning.

[7]  Leon van der Torre,et al.  Preference-based argumentation: Arguments supporting multiple values , 2008, Int. J. Approx. Reason..

[8]  Jordi Cabot,et al.  MoDisco: A model driven reverse engineering framework , 2014, Inf. Softw. Technol..

[9]  Henri Prade,et al.  Decision-Making Process: Concepts and Methods , 2009 .

[10]  Jean-François Baget,et al.  Rules Dependencies in Backward Chaining of Conceptual Graphs Rules , 2006, ICCS.

[11]  Madalina Croitoru,et al.  Visual reasoning with graph-based mechanisms: the good, the better and the best , 2013, The Knowledge Engineering Review.

[12]  Zainab Assaghir,et al.  Numerical Information Fusion: Lattice of Answers with Supporting Arguments , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[13]  Stefan Schlobach,et al.  ABox Abduction in the Description Logic $\boldsymbol{\mathcal{ALC}}$ , 2010, Journal of Automated Reasoning.

[14]  Anthony Hunter,et al.  Practical First-Order Argumentation , 2005, AAAI.

[15]  Marie-Laure Mugnier,et al.  Graph-based Knowledge Representation - Computational Foundations of Conceptual Graphs , 2008, Advanced Information and Knowledge Processing.

[16]  Sophie Dubuisson-Quellier,et al.  De la routine à la délibération. Les arbitrages des consommateurs en situation d'achat , 2006 .

[17]  Cédric Baudrit,et al.  Modelling and analysis of complex food systems: State of the art and new trends , 2011 .

[18]  Stefan Schlobach,et al.  ABox Abduction in the Description Logic ALC , 2011, J. Autom. Reason..

[19]  Guillermo Ricardo Simari,et al.  Defeasible logic programming: an argumentative approach , 2003, Theory and Practice of Logic Programming.

[20]  Jean-Marie Bourre,et al.  Valeur nutritionnelle (macro et micro-nutriments) de farines et pains français , 2008 .

[21]  Phan Minh Dung,et al.  On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games , 1995, Artif. Intell..

[22]  Anthony Hunter,et al.  Elements of Argumentation , 2007, ECSQARU.

[23]  Sylvie Issanchou,et al.  Effect of fibre information on consumer willingness to pay for French baguettes , 2009 .

[24]  Andrea Calì,et al.  Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications , 2010, 2010 25th Annual IEEE Symposium on Logic in Computer Science.

[25]  J. Slavin,et al.  Dietary fibre and satiety , 2007 .

[26]  Guillermo Ricardo Simari,et al.  Argumentation in Artificial Intelligence , 2009 .

[27]  Michaël Thomazo,et al.  A Sound and Complete Backward Chaining Algorithm for Existential Rules , 2012, RR.

[28]  Henri Prade,et al.  Using arguments for making and explaining decisions , 2009, Artif. Intell..

[29]  M. Chein,et al.  Graph-based Knowledge Representation and Reasoning , 2010, ICEIS.

[30]  Franz Baader,et al.  Pushing the EL Envelope , 2005, IJCAI.

[31]  Jean-Rémi Bourguet,et al.  Contribution aux méthodes d'argumentation pour la prise de décision. Application à l'arbitrage au sein de la filière céréalière. (Contribution to the methods of argumentation for decision making. Application to arbitration within the cereal industry) , 2010 .

[32]  Marie-Laure Mugnier,et al.  An artificial intelligence-based approach to deal with argumentation applied to food quality in a public health policy , 2013, Expert Syst. Appl..

[33]  Martin Caminada,et al.  On the evaluation of argumentation formalisms , 2007, Artif. Intell..