Nouredine Tamani

Fuzzy bipolar conditions of type ”or else”

Previously studied fuzzy bipolar conditions of type ”and if possible” are made of a mandatory condition c and an optional condition w. They allow expressing complex preferences of a conjunctive nature. We define in this paper, a new kind of fuzzy bipolar conditions of the form ”or else” which express complex preferences of a disjunctive nature. We show that the ”or else” form can be used as a negation operator of the ”and if possible” form and vice versa. We also show that these both forms are compatible and, therefore, fuzzy bipolar conditions of both types can be used together in the same bipolar query.

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.

A Decision Support System to design modified atmosphere packaging for fresh produce based on a bipolar flexible querying approach

We define a multi-criteria Decision Support System for designing fresh food packaging.A modified atmosphere packaging simulation module is included in the DSS.A flexible querying module handles imprecise data stored in a packaging database.Using the DSS the user will have only one trial to perform validation step. To design new packaging for fresh food, stakeholders of the food chain express their needs and requirements, according to some goals and objectives. These requirements can be gathered into two groups: (i) fresh food related characteristics and (ii) packaging intrinsic characteristics. Modified Atmosphere Packaging (MAP) is an efficient way to delay senescence and spoilage and thus to extend the very short shelf life of respiring products such as fresh fruits and vegetables. Consequently, packaging O2/CO2 permeabilities must fit the requirements of fresh fruits and vegetable as predicted by virtual MAP simulating tools. Beyond gas permeabilities, the choice of a packaging material for fresh produce includes numerous other factors such as the cost, availability, potential contaminants of raw materials, process ability, and waste management constraints. For instance, the user may have the following multi-criteria query for his/her product asking for a packaging with optimal gas permeabilities that guarantee product quality and optionally a transparent packaging material made from renewable resources with a cost for raw material less than 3?/kg. To help stakeholders taking a rational decision based on the expressed needs, a new multi-criteria Decision Support System (DSS) for designing biodegradable packaging for fresh produce has been built. In this paper we present the functional specification, the software architecture and the implementation of the developed tool. This tool includes (i) a MAP simulation module combining mass transfer models and respiration of the food, (ii) a multi-criteria flexible querying module which handles imprecise, uncertain and missing data stored in the database. We detail its operational functioning through a real life case study to determine the most satisfactory materials for apricots packaging.

A Fuzzy Ontology for Database Querying with Bipolar Preferences

The expression and the evaluation of complex user preferences in the context of distributed and heterogeneous information systems are tackled in this paper. Complex preferences are modeled by fuzzy bipolar conditions, which associate negative and positive conditions. Queries involving such conditions are called bipolar queries. In our case, such queries are addressed to information systems such as Web applications, built on several distributed and heterogeneous databases. This querying can lead to process huge volumes of data and can deliver massive responses, in which it is difficult to the user to distinguish the relevant answers from irrelevant ones. Semantic aspects make it possible to address this problem by providing a personalized data access method, so that only the most relevant data are targeted to evaluate queries. We introduce then in this paper a new approach for flexible querying of complex information systems that combines a reasoning mechanism (an ontology‐based on the fuzzy bipolar DLR‐Lite) with a bipolar relational language of a high expressivity (Bipolar SQLf language). The reasoning mechanism can also answer queries in approximative way, based on degrees expressing at which extent it is possible to substitute a concept in the query with other concepts, while still meaningful to the user.

Bipolar SQLf: a flexible querying language for relational databases

Flexible querying of information systems allows expressing complex preferences in user queries. Such preferences can be modeled by fuzzy bipolar conditions which are made of constraints c and wishes w and interpreted as ”to satisfy c and if possible to satisfy w”.We define in this article the main elements of the Bipolar SQLf language, which is an SQL-like querying language based on a bipolar relational algebra [11,3]. This language is an extension of the SQLf language [2,1]. Basic statements (projection, selection, etc.) are firstly defined in terms of syntax, evaluation and calibration. Then, complex statements, such as bipolar queries based on nesting operators are studied in terms of expression, evaluation, query equivalence and backward compatibility with the SQLf language.

Query Failure Explanation in Inconsistent Knowledge Bases: A Dialogical Approach

In the EcoBioCap project (www.ecobiocap.eu) about the next generation of packaging, a decision support system has been built that uses argumentation to deal with stakeholder preferences. However, when testing the tool the domain experts did not always understand the output of the system. The approach developed in this paper is the first step to the construction of a decision support system endowed with an explanation module. We place ourselves in the equivalent setting of inconsistent Ontology-Based Data Access (OBDA) and addresses the problem of explaining Boolean Conjunctive Query (BCQ) failure. Our proposal relies on an interactive and argumentative approach where the processes of explanation takes the form of a dialogue between the User and the Reasoner. We exploit the equivalence between argumentation and inconsistency tolerant semantics to prove that the Reasoner can always provide an answer for user’s questions.

Fuzzy Argumentation System for Decision Support

We introduce in this paper a quantitative preference based argumentation system relying on ASPIC argumentation framework and fuzzy set theory. The knowledge base is fuzzified to allow the experts to express their expertise (premises and rules) attached with grades of importance in the unit interval. Arguments are attached with a score aggregating the importance expressed on their premises and rules. Extensions are then computed and the strength of each of which can also be obtained based on its strong arguments. The strengths are used to rank fuzzy extensions from the strongest to the weakest one, upon which decisions can be made. The approach is finally used for decision making in a real world application within the EcoBioCap project.

A quantitative preference-based structured argumentation system for decision support

We introduce in this paper a quantitative preference based argumentation system relying on ASPIC argumentation framework [1] and fuzzy set theory. The knowledge base is fuzzified to allow agents expressing their expertise (premises and rules) attached with grades of importance in the unit interval. Arguments are then attached with a strength score aggregating the importance expressed on their premises and rules. Extensions, corresponding to subsets of consistent arguments, are also attached with forces computed based on their strong arguments. The forces are used then to rank extensions from the strongest to the weakest one, upon which decisions can be made. We have also shown that the strength preference relation defined over arguments is reasonable [2] and our fuzzy ASPIC argumentation system can be seen as a computationally efficient instantiation of the generic model of structured argumentation framework introduced in [2].

Bipolarity and the relational division

A fuzzy bipolar relation is a relation defined by a fuzzy bipolar condition, which could be interpreted as an association of a constraint and a wish. In this context, the extension of the relational division operation to bipolarity is studied in this paper. Firstly, we define a bipolar division when the involved relations are crisp. Then, we define, from the semantic point of view, several forms of bipolar division when the involved relations are defined by fuzzy bipolar conditions. These forms of division can be related to the general model of fuzzy bipolar division introduced by Bosc and Pivert.

Query Answering Explanation in Inconsistent Datalog

The paper addresses the problem of explaining Boolean Conjunctive Query BCQ entailment in the presence of inconsistency within the Ontology-Based Data Access OBDA setting, where inconsistency is handled by the intersection of closed repairs semantics ICR and the ontology is represented by Datalog