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

Real-time Elective Admissions Planning for Health Care Providers

Efficient management of patient admissions plays a critical role in increasing a hospital's resource utilization and reducing health care costs. We consider the problem of fi nding the best available admission policy for elective hospital admissions under real time constraints. The problem is modeled as a Markov Decision Process (MDP) and we investigate current state-of-the art real time planning methods. Due to the complexity of the model, traditional mode-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. Results show that given reasonable resources, our approach generates improved deci- sions over existing alternatives that fail to scale as model complexity increases. We also propose a parameter tuning method that can be easily and efficiently implemented

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

An UCT Approach for Anytime Agent-based Planning

Abstract In this paper, we introduce a new heuristic search algorithm based on mean values for anytime planning, called MHSP. It consists in associating the principles of UCT, a bandit-based algorithm which gave very good results in computer games, and especially in Computer Go, with heuristic search in order to obtain an anytime planner that provides partial plans before finding a solution plan, and furthermore finding an optimal plan. The algorithm is evaluated in different classical planning problems and compared to some major planning algorithms. Finally, our results highlight the capacity of MHSP to return partial plans which tend to an optimal plan over the time.

Monte-Carlo tree search using expert knowledge: an application to computer go and human genetics

Monte-Carlo Tree Search (MCTS la búsqueda en árbol mediante procesos estocásticos) se ha convertido en el algorítmo principal en muchos problemas de inteligencia artificial e informática. Esta tesis analiza la incorporación de conocimiento experto para mejorar la búsqueda. El trabajo describe dos aplicaciones: una en el 'juego del go' por el ordenador y otra en el campo de la genética humana. Es un hecho establecido que, en problemas complejos, MCTS requiere el apoyo de conocimiento específico o aprendido online para mejorar su rendimiento. Lo que este trabajo analiza son diferentes ideas de cómo hacerlo, sus resultados e implicaciones, mejorando así nuestra comprensión de MCTS. Las principales contribuciones al área son: un modelo analítico de las simulaciones que mejora la comprensión del papel de las simulaciones, un marco competitivo incluyendo código y datos para comparar métodos en etiología genética y tres aplicaciones con éxito: una en el campo de las aperturas en go de 19x19 llamada M-eval, otra sobre simulaciones que aprenden y una en etiología genética. Además, merece la pena destacar: un modelo para representar proporciones mediante estados llamado WLS con software libre, un resultado negativo sobre una idea para las simulaciones, el descubrimiento inesperado de un posible problema utilizando MCTS en optimización y un análisis original de las limitaciones