Karima Sedki

A Bayesian network for recurrent multi-criteria and multi-attribute decision problems: Choosing a manual wheelchair

This paper discusses recurrent multi-criteria, multi-attribute decision problems. Because of the possibility of decision-maker ignorance or low decision-maker involvement the decision problem structuring is done once for all by a group of experts and does not involve the implication of the decision makers. We propose an original model based on Bayesian networks, which provides a decision process that helps the decision-maker to select an appropriate alternative among a set of alternatives, taking into account multiple criteria that are often conflicting. Our model makes it possible to represent in the same model the decision case (i.e., the decision-maker characteristics, contextual characteristics, their needs and preferences), the set of alternatives with the different attributes, and the choice criteria. The model allows us to compute the value of three essential elements: the importance of each criterion, which is based on the decision-case characteristics; each criterions evaluation index in terms of the alternative; and each criterions satisfaction index. The recurrent problem of choosing a manual wheelchair (MWC) illustrates the construction and use of our model.

Inference reasoning on fishers' knowledge using Bayesian causal maps

Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individuals judgments, drawing inferences in cog-nitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity , we derive a decision facilitating tool, enabling scenarios testing for fisheries management.

A MODEL BASED ON INFLUENCE DIAGRAMS FOR MULTI-CRITERIA DECISION-MAKING

In this paper, we focus on multi-criteria decision-making problems. We propose a model based on influence diagrams; this model is able to handle uncertainty, represent interdependencies among the different decision variables and facilitate communication between the decision-maker and the analyst. The particular structure of the proposed model makes it possible to take into account the alternatives described by an attribute set, the decision-makers characteristics and preferences, and other information (e.g., internal or external factors) that influence the decision. Modeling the decision problem in terms of influence diagrams requires a lot of work to gather expert knowledge. However, once the model is built, it can be easily and efficiently used for different instances of the decision problem. In fact, using our model simply requires entering some basic information, such as the values of internal or external factors and the decision-makers characteristics. Our model also defines the importance of each criterion in terms of what is known about the decision maker, the quality index and the utility of each alternative.

Formalizing Arguments From Cause-Effect Rules.

This paper proposes a method allowing the formalisation of cause-effect rules that are reported by expert’s knowledge as argumentation framework. The rules represent causal relation between two given concepts. Such rules have the advantage to be easily elicited by domain experts, however the inference mechanism is rather ad hoc and there is no good theoretical foundation. The objective of the proposition is to overcome the major limit of the reported cause-effect rules, that is the need of efficient reasoning.

Data Analytics and Visualization for Connected Objects: A Case Study for Sleep and Physical Activity Trackers

In recent years, a large number of connected objects for the monitoring of activities (health and well-being, sleep, fitness, nutrition, etc.) have emerged and are very popular with the general public. No doubt that their price, their ease of use and their interest in their health and well-being contribute to this success around the world. However, many of these consumer-connected objects suffer from several limitations, especially with regard to their high-level or smart functionalities and the added value of the information provided by such objects. In this paper, we first focus on such limitations then provide a real case study in monitoring physical activities and sleep using Fitbit smart watches. We propose some high level functionalities by taking advantage of the large amount of data collected and using data analytics and visualization techniques.

Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy

Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also state that there is a high bacteria resistance in this context. In this paper, we propose a method for the semi-automatic detection of inconsistencies in guidelines using preference learning, and we apply this method to antibiotherapy in primary care. The preference model was learned from the recommendations and from a knowledge base describing the domain. We successfully built a generic model suitable for all infectious diseases and patient profiles. This model includes both preferences and necessary features. It allowed the detection of 106 candidate inconsistencies which were analyzed by a medical expert. 55 inconsistencies were validated. We showed that therapeutic strategies of guidelines in antibiotherapy can be formalized by a preference model. In conclusion, we proposed an original approach, based on preferences, for modeling clinical guidelines. This model could be used in future clinical decision support systems for helping physicians to prescribe antibiotics.

Constrained Value-Based Argumentation Framework

Value-based argumentation framework (VAF) is an extension of Dung argumentation framework where arguments promote specific values. In VAF, an argument a defeats b only if the value promoted by b is not preferred than the value promoted by a according to some total ordering on values given by a specific audience. However, despite the interesting idea of considering the preference relation between arguments’ values, VAF does not offer a way to express further requirements, like “no arguments promoting expensive value” or “if we accept arguments promoting expensive value, then we accept arguments promoting healthy value”. This paper extends VAF by incorporating some constraints, expressed as propositional formulas on either the arguments’ values or on the arguments. We propose two inference relations for defining some acceptability semantics in such constrained value-based argumentation framework (CVAF). The first inference relation is more prudent than the second one since it derives less arguments.

Value-based argumentation framework built from prioritized qualitative choice logic☆

Abstract The notion of preference is crucial in many fields. This justifies the development of many formalisms for preferences representation such as CP-nets, qualitative choice logic and its extensions. Preferences help to choose the best option in decision making, to compare between arguments in argumentation theory, etc. In this paper, we establish a link between a preference formalism, called Prioritized Qualitative Choice Logic (PQCL) and argumentation theory. We show that for any set of preferences expressed using PQCL (called PQCL theory), a Value-based Argumentation Framework (VAF) can be built. However, we point out some problems related to the evaluation of arguments which does not guarantee the correspondence between elements of PQCL theory and those of its associated VAF. We show that the major problem is due to the evaluation of arguments defined in existing argumentation frameworks, where an absolute status is assigned to each argument: objectively (or skeptically) accepted if it belongs to every extension, subjectively (or credulously) accepted if it is in some extensions and not in others and rejected if it does not belong to any extension. To deal with this problem, we propose to revise the evaluation of arguments in the corresponding VAF. As a result, there is a direct relationship between preferred extensions of the corresponding VAF and preferred models of a set of preferences expressed using PQCL. In addition, rank ordering the set of arguments is possible. The relationship between the two formalisms is interesting since on the one hand, it points out that one should be careful in using argumentation theory for decision making purposes or in formalizing a given problem as an argumentation framework and on the other hand, it makes it possible to use an argumentation framework for preference elicitation.