Cédric Baudrit

Representing parametric probabilistic models tainted with imprecision

Numerical possibility theory, belief functions have been suggested as useful tools to represent imprecise, vague or incomplete information. They are particularly appropriate in uncertainty analysis where information is typically tainted with imprecision or incompleteness. Based on their experience or their knowledge about a random phenomenon, experts can sometimes provide a class of distributions without being able to precisely specify the parameters of a probability model. Frequentists use two-dimensional Monte-Carlo simulation to account for imprecision associated with the parameters of probability models. They hence hope to discover how variability and imprecision interact. This paper presents the limitations and disadvantages of this approach and propose a fuzzy random variable approach to treat this kind of knowledge.

A Decision Support System Coupling Fuzzy Logic and Probabilistic Graphical Approaches for the Agri-Food Industry: Prediction of Grape Berry Maturity.

Agri-food is one of the most important sectors of the industry and a major contributor to the global warming potential in Europe. Sustainability issues pose a huge challenge for this sector. In this context, a big issue is to be able to predict the multiscale dynamics of those systems using computing science. A robust predictive mathematical tool is implemented for this sector and applied to the wine industry being easily able to be generalized to other applications. Grape berry maturation relies on complex and coupled physicochemical and biochemical reactions which are climate dependent. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert predictions. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a decision support system so called FGRAPEDBN able to (1) capitalize the heterogeneous fragmented knowledge available including data and expertise and (2) predict the sugar (resp. the acidity) concentrations with a relevant RMSE of 7 g/l (resp. 0.44 g/l and 0.11 g/kg). FGRAPEDBN is based on a coupling between a probabilistic graphical approach and a fuzzy expert system.

Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP

Industrial agrifood processes often strongly rely on human expertise, expressed as know-how and control procedures based on subjective measurements (color, smell, texture), which are very difficult to capture and model. We deal in this paper with a cheese ripening process (of french Camembert), for which experimental data have been collected within a cheese ripening laboratory chain. A global and a monopopulation cooperative/coevolutive GP scheme (Parisian approach) have been developed in order to simulate phase prediction (i.e. a subjective estimation of human experts) from microbial proportions and Ph measurements. These two GP approaches are compared to Bayesian network modeling and simple multilinear learning algorithms. Preliminary results show the effectiveness and robustness of the Parisian GP approach.

A Dynamic Bayesian Network to Represent a Ripening Process of a Soft Mould Cheese

Available knowledge to describe food processes has been capitalized from different sources, is expressed under different forms and at different scales. To reconstruct the puzzle of knowledge by taking into account uncertainty, we need to combine, integrate different kinds of knowledge. Mathematical concepts such that expert systems, neural networks or mechanistic models reach operating limits. In all cases, we are faced with the limits of available data, mathematical formalism and the limits of human reasoning. Dynamical Bayesian Networks (DBNs) are practical probabilistic graphic models to represent dynamical complex systems tainted with uncertainty. This paper presents a simplified dynamic bayesian networks which allows to represent the dynamics of microorganisms in the ripening of a soft mould cheese (Camembert type) by means of an integrative sensory indicator. The aim is the understanding and modeling of the whole network of interacting entities taking place between the different levels of the process.

A probabilistic graphical model for describing the grape berry maturity

Dynamic Bayesian networks for coupling heterogeneous data and expertise knowledge.The modeling of grape berry maturity over the time tainted with uncertainty.Prediction of sugar, acidity and anthocyanin concentrations over the maturity. Grape berry maturation depends on complex and coupled physiological and biochemical reactions which are climate dependant. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert prediction. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a robust mathematical model able (1) to capitalize the heterogeneous fragmented available knowledge including data and expertise by means of probabilistic graphical approaches; and (2) to predict sugar, acidity and anthocyanin concentrations over the maturity.

Unifying parameter learning and modelling complex systems with epistemic uncertainty using probability interval

Knowledge regarding complex systems are heterogeneous and fragmented.modelling dynamic complex systems in the framework of dynamic credal networks.practical methodology coupling Dirichlet distributions with interval probabilities to incrementally build and update model parameters whatever source and format of knowledge.enables to take into account (1) stochastic and epistemic uncertainties pertaining to the system; (2) the confidence level on the different sources of information.illustrate the application of the methodology to the modelling of a simplified industrial case study. Modeling complex dynamical systems from heterogeneous pieces of knowledge varying in precision and reliability is a challenging task. We propose the combination of dynamical Bayesian networks and of imprecise probabilities to solve it. In order to limit the computational burden and to make interpretation easier, we also propose to encode pieces of (numerical) knowledge as probability intervals, which are then used in an imprecise Dirichlet model to update our knowledge. The idea is to obtain a model flexible enough so that it can easily cope with different uncertainties (i.e., stochastic and epistemic), integrate new pieces of knowledge as they arrive and be of limited computational complexity.

Cooperative Coevolution for Agrifood Process Modeling

On the contrary to classical schemes of evolutionary optimisations algorithms, single population Cooperative Co-evolution techniques (CCEAs, also called “Parisian” approaches) make it possible to represent the evolved solution as an aggregation of several individuals (or even as a whole population). In other words, each individual represents only a part of the solution. This scheme allows simulating the principles of Darwinian evolution in a more economic way, which results in gain in robustness and efficiency. The counterpart however is a more complex design phase. In this chapter, we detail the design of efficient CCEAs schemes on two applications related to the modeling of an industrial agri-food process. The experiments correspond to complex optimisations encountered in the modeling of a Camembert-cheese ripening process. Two problems are considered:

Knowledge book on lignocellulose deconstruction: an INRA project to identify key actions in research on biorefineries

The existence of pilot and industrial scale biorefineries worldwide demonstrates the technical and economic feasibility of fractionating lignocellulose (LC) for chemistry and energy. This raises new questions about the biomass supply, management of its quality and about the elementary step combination in processes, to choose which compound will be main- or co-products from plant biomass. Integrated and systemic approaches are requested to invent and/or to improve the biotechnical fractionation of LCs and there is a need to collect and correlate the existing knowledge in a structured way, to gain a better insight of the overall process. Building such a knowledge representation is important for scientists, research institutes, universities and industries, as it will give a shared description of the knowledge in that field, that will further facilitate its diffusion, re-use, review, reassessment and updating with new findings. Practically, an extensive literature has been published in the past five years on the biorefinery of LC, focusing mostly on the saccharification of polysaccharides (cellulose, hemicellulose). As a consequence, most of the data available results from biochemical and physicochemical analyzes from several processing chains, which combine different modes of physical pretreatments and/or chemical typologies of variables biomass and/or various hydrolytic enzyme cocktails. That is why a project for development of a hypertext electronic Knowledge Book on LignoCellulose DeConstruction (KB-LCDC) was initiated by the French National Institute for Agricultural Research (INRA) with two main goals: i/ to elicit the available knowledge from various sources, more specifically related to the enzymatic hydrolysis of wheat straw into glucose, ii/ to represent the knowledge and implement it into a web-based format of Knowledge Book (KB) taking into account the overall saccharification process. The knowledge was first elicited by means of semi-structured interviews with a group of six experts working in several INRA research Units, and involved in the Institute’s biomass transformation network. Concomitantly, the collating of data and knowledge from “grey-” and peer reviewed- literature was also done. Then, our approach consists in building a knowledge book (KB) whose pages are formatted concept maps (Cmap) and technical sheets that are connected by hypertext links. A Cmap is a semantic graph where nodes represent concepts that are connected by arcs expressing relationships between them. A formatted Cmap answers a specific question about one central concept (for instance: what is the impact of the pretreatment on the reactivity of biomass ? How does enzyme diffuse into the lignocellulose ?). Hyperlinks existing between Cmaps and technical sheets form a network of knowledge, into which the user can navigate, to find relevant answers, but also associated concepts. Hyperlinks can also link Cmaps or technical sheet to an Internet page, scientific article and any document selected to illustrate the reality of a concept. Up to nine knowledge areas have been identified so far, among them: biomass pretreatment; separation methods; enzyme cocktails; substrate reactivity, hydrolysis mechanisms. A global representation of the overall process from wheat straw to glucose, based on the individual Cmaps, has been built. It includes a static structural view (environment and reactivity of the media, encompassing the cell wall); a dynamic view (hierarchy of the different sub-processes at work) and a functional view (description of the elementary steps and how they are organized in time). In the frame of the LBT III congress, the practical structuration of the knowledge and the original version of the KB will be disclosed. The potential development and use of this new approach for the representation of biotechnology processes applied to LC will be discussed (process workflow; unlocking cell wall recalcitrance; strategic roadmaps).