Planifier lorsque le but change. Une approche inspirée de la recherche de cible mouvante

National audienceIn this paper, we propose a novel planner, called Moving Goal Planner (MGP) in order to adapt plans when the goal changes over time. This planner draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible starting new searches when the goal changes. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing the actions of the current plan brings MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to adapt incrementally the search tree at each new search that reduces the number of calls to the heuristic function and speeds up the search. Finally, we show evaluation results that demonstrate the effectiveness of our approach.Dans cet article, nous proposons un nouvel algorithme de planification temps réel ap- pelé MGP (Moving Goal Planning) capable de s'adapter lorsque le but évolue dynamiquement au cours du temps. Cet algorithme s'inspire des algorithmes de type Moving Target Search (MTS). Afin de réduire le nombre de recherches effectuées et améliorer ses performances, MGP retarde autant que possible le déclenchement de nouvelles recherches lorsque que le but change. Pour cela, MGP s'appuie sur deux stratégies : Open Check (OC) qui vérifie si le nouveau but est présent dans l'arbre de recherche déjà construit lors d'une précédente recherche et Plan Follow (PF) qui estime s'il est préférable d'exécuter les actions du plan courant pour se rapprocher du nouveau but plutôt que de relancer une nouvelle recherche. En outre, MGP utilise une stratégie "conservatrice" de mise à jour incrémentale de l'arbre de recherche lui permettant de réduire le nombre d'appels à la fonction heuristique et ainsi d'accélérer la recherche d'un plan solu- tion. Finalement, nous présentons des résultats expérimentaux qui montrent l'efficacité de notre approche

A new approach for continual planning

International audienceDevising intelligent robots or agents that interact with humans is a major challenge for artificial intelligence. In such contexts, agents must constantly adapt their decisions according to human activities and modify their goal. In this extended abstract, we present a novel continual planning approach, called Moving Goal Planning (MGP) to adapt plans to goal evolutions. This approach draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible the start of new searches when the goal changes over time. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing actions of the current plan brings MGP closer to the new goal

Recherche heuristique basée sur le calcul de moyenne pour la planification temps réel

National audienceDans cet article, nous présentons un nouvel algorithme de recherche heuristique basé sur le calcul de moyennes pour la planification temps réel, appelé MHSP (Mean-Based Heuristic Search Planning). Il associe les principes d'UCT (Upper Confidence for Tree), un algorithme de type bandit ayant donné de très bons résultats dans le domaine des jeux, et plus particulièrement dans le cadre du jeu de Go, et une recherche heuristique en vue d'obtenir un planificateur temps réel dans le contexte de la planification d'actions. MHSP est évalué sur différents problèmes de planification et comparé aux algorithmes de recherche en ligne et d'apprentissage existants. Nos résultats mettent en évidence la capacité de MHSP à retourner en temps réel des plans qui tendent vers un plan optimal au cours du temps. Ils montrent de plus que MHSP est plus rapide et retourne des plans de meilleure qualité que les algorithmes existants dans la littérature

A new approach for continual planning

International audienceDevising intelligent robots or agents that interact with humans is a major challenge for artificial intelligence. In such contexts, agents must constantly adapt their decisions according to human activities and modify their goal. In this extended abstract, we present a novel continual planning approach, called Moving Goal Planning (MGP) to adapt plans to goal evolutions. This approach draws inspiration from Moving Target Search (MTS) algorithms. In order to limit the number of search iterations and to improve its efficiency, MGP delays as much as possible the start of new searches when the goal changes over time. To this purpose, MGP uses two strategies: Open Check (OC) that checks if the new goal is still in the current search tree and Plan Follow (PF) that estimates whether executing actions of the current plan brings MGP closer to the new goal

Longitudinal liver stiffness assessment in patient with chronic hepatitis C undergoing antiviral therapy.

BACKGROUND/AIMS:Liver stiffness (LS) measurement by means of transient elastography (TE) is accurate to predict fibrosis stage. The effect of antiviral treatment and virologic response on LS was assessed and compared with untreated patients with chronic hepatitis C (CHC). METHODS: TE was performed at baseline, and at weeks 24, 48, and 72 in 515 patients with CHC. RESULTS: 323 treated (62.7%) and 192 untreated patients (37.3%) were assessed. LS experienced a significant decline in treated patients and remained stable in untreated patients at the end of study (P<0.0001). The decline was significant for patients with baseline LS ≥ 7.1 kPa (P<0.0001 and P 0.03, for LS ≥ 9.5 and ≥ 7.1 kPa vs lower values, respectively). Sustained virological responders and relapsers had a significant LS improvement whereas a trend was observed in nonresponders (mean percent change -16%, -10% and -2%, for SVR, RR and NR, respectively, P 0.03 for SVR vs NR). In multivariate analysis, high baseline LS (P<0.0001) and ALT levels, antiviral therapy and non-1 genotype were independent predictors of LS improvement. CONCLUSIONS: LS decreases during and after antiviral treatment in patients with CHC. The decrease is significant in sustained responders and relapsers (particularly in those with high baseline LS) and suggests an improvement in liver damage