Model-Checking an Ecosystem Model for Decision-Aid

International audience—This work stems on the idea that timed automata models and model-checking techniques may bring much in a decision-aid context when dealing with large and interacting qualitative models. In this paper, we focus on two key issues when facing the interpretation and explanation of behavior in real-world systems: the model building and its exploration using logic patterns. We illustrate this approach in the ecological domain with the modeling and exploration of a fisheries ecosystem

Efficient trajectories computing using inversibilities properties

A time-consuming problem encountered both in system diagnosis and planning is that of computing trajectories over a behavioral model. In order to improve the efficiency of this task, there is currently a great interest in using model-checking techniques developed within the area of computer aided verification. In this paper, we propose to represent the system as automata and we define a property called inversibility. This property is used to improve the efficiency of the search algorithm computing trajectories. We present two study cases in diagnosis and planning domains where this approach gives satisfactory results

Temporal Planning with extended Timed Automata

International audienceWe consider a system modeled as a set of interacting agents evolving along time according to explicit timing constraints. In this kind of system, the planning task consists in selecting and organizing actions in order to reach a goal state in a limited time and in an optimal manner, assuming actions have a cost. We propose to reformulate the planning problem in terms of model-checking and controller synthesis on interacting agents such that the state to reach is expressed using temporal logic. We have chosen to represent each agent using the formalism of Priced Timed Game Automata (PTGA). PTGA is an extension of Timed Automata that allows the representation of cost on actions and uncontrollable actions. Relying on this domain description, we define a planning algorithm that computes the best strategy to achieve the goal. This algorithm is based on recognized model-checking and synthesis tools from the UPPAAL suite. The expressivity of this approach is evaluated on the classical Transport Domain which is extended in order to include timing constraints, cost values and uncontrollable actions. This work has been implemented and performances evaluated on benchmarks

Searching for Cost-Optimized Strategies: An Agricultural Application

Best Paper AwardInternational audienceWe consider a system modeled as a set of interacting components evolving along time according to explicit timing constraints. The decision making problem consists in selecting and organizing actions in order to reach a goal state in a limited time and in an optimal manner, assuming actions have a cost. We propose to reformulate the planning problem in terms of model-checking and controller synthesis such that the state to reach is expressed using a temporal logic. We have chosen to represent each agent using the formalism of Priced Timed Game Au-tomata (PTGA) and a set of knowledge. PTGA is an extension of Timed Automata that allows the representation of cost on actions and the definition of a goal (to reach or to avoid). This paper describes two algorithms designed to answer the planning problem on a network of agents and proposes practical implementation using model-checking tools that shows promising results on an agricultural application: a grassland based dairy production system

Répondre aux questions "Que faire pour" par synthèse de contrôleur sur des automates temporisés - Application à la gestion de la pêche

Session "Posters"National audienceNous montrons dans cet article comment répondre à des questions de type "Que faire pour éviter telle situation ?" (requête de sûreté) en nous appuyant sur une modélisation qualitative sous forme d'automates temporisés et en utilisant des outils de model-checking. Une approche exploitant la synthèse de contrôleur est comparée à une approche de type "Générer et tester". L'application qui motive ce travail est celle de la gestion d'un écosystème marin et l'élaboration de politiques de gestion de pêch

TAG: Learning Timed Automata from Logs

International audienceEvent logs are often one of the main sources of information to understand the behavior of a system. While numerous approaches have extracted partial information from event logs, in this work, we aim at inferring a global model of a system from its event logs. We consider real-time systems, which can be modeled with Timed Automata: our approach is thus a Timed Automata learner. There is a handful of related work, however, they might require a lot of parameters or produce Timed Automata that either are undeterministic or lack precision. In contrast, our proposed approach, called TAG, requires only one parameter and learns a deterministic Timed Automaton having a good tradeoff between accuracy and complexity of the automata. This allows getting an interpretable and accurate global model of the real-time system considered. Our experiments compare our approach to the related work and demonstrate its merits

Algorithme de prédiction en temps réel de la consommation alimentaire journalière chez la truie en lactation

International audienceDeveloping algorithms able to predict daily feed intake is essential for implementing precision-feeding strategies in real time. Given the lack of a mechanistic model to predict feed intake in lactating sows, a new approach that combined real-time prediction with off-line learning of sow feeding behaviours was developed. A database of 39,090 lactations from 6 farms that contained the first 20 post-farrowing feed intake values was used to (i) identify groups of sows with similar feeding behaviour and (ii) test three functions to predict feed intake. The homogeneity of clusters obtained by off-line learning was assessed according to the Silhouette and Calinski-Harabasz scores. The prediction functions were evaluated by calculating mean error (ME) and root mean square error (RMSE) per day and per sow. The clusters with the best homogeneity were obtained by dividing the database into two groups. The trajectory of feed intake of the first group increased continuously during lactation, while that of the second plateaued from day 10 onwards. The ME per sow obtained for these two trajectories using the best function was-0.08 kg/d, and the corresponding RMSE was 1.06 kg/d. Although individual variability was high, the use of trajectories improved the prediction of feed intake. In practice, learning of trajectories may be recalculated regularly, while the real-time prediction function, which requires fewer computing resources, could be embedded into the smart feeder.Le développement d'algorithmes de prédiction de la consommation alimentaire journalière est essentiel à la mise en oeuvre des stratégies d'alimentation de précision en temps réel. Compte tenu de l'absence de modèle mécaniste de prédiction de la consommation chez la truie en lactation, une nouvelle approche est proposée combinant une prédiction en temps réel avec un apprentissage « hors ligne » des comportements alimentaires de la truie. Une base de données de 39 090 lactations, provenant de six exploitations et contenant les consommations des 20 jours après mise-bas, a été utilisée pour (i) identifier des groupes (clusters) de truies présentant un comportement alimentaire similaire et (ii) tester trois fonctions de prédiction de la consommation. L'homogénéité des clusters obtenus par apprentissage « hors ligne » a été évaluée selon les indices Silhouette et Calinski-Harabasz. Les méthodes de prédiction ont été évaluées avec l'erreur moyenne et l'erreur quadratique moyenne (RMSE) déterminées par jour et par truie. Les clusters les plus homogènes sont obtenus lorsque la base est divisée en deux groupes. La trajectoire du premier groupe est caractérisée par une augmentation continue de la consommation au cours de la lactation, et la seconde, par un plateau atteint à partir du 10 ème jour. L'erreur moyenne par truie, obtenue en utilisant deux trajectoires et la meilleure fonction de prédiction, est de-0,08 kg/j, avec une RMSE de 1,06 kg/j. Bien que la variabilité individuelle soit élevée, l'utilisation des trajectoires améliore la prédiction de la consommation. En pratique, l'apprentissage des trajectoires peut être renouvelé régulièrement, tandis que la procédure de prédiction, peu gourmande en puissance de calcul, peut être intégrée dans le système d'alimentation de précision