Probabilistic Planning

Résolution efficace de processus décisionnels de Markov par l'exploitation d'approches structurelles et algorithmiques tirant parti de l'architecture moderne des ordinateurs
Cette thèse présente des contributions en planification automatique sous incertitude, un domaine de l’intelligence artificielle. …
Jaël Champagne Gareau
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Towards Topologically Diverse Probabilistic Planning Benchmarks
Markov Decision Processes (MDPs) are often used in Artificial Intelligence to solve probabilistic sequential decision-making problems. …
Jaël Champagne Gareau, Éric Beaudry, Vladimir Makarenkov
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Markov Decision Processes
This project aimed at finding different ways to improve (SSP-)MDP planners performance when considering computer architectures (e.g., cache-memory, parallelism)
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Markov Decision Processes
pcTVI: Parallel MDP Solver Using a Decomposition Into Independent Chains
Markov Decision Processes (MDPs) are useful to solve real-world probabilistic planning problems. However, finding an optimal solution …
Jaël Champagne Gareau, Éric Beaudry, Vladimir Makarenkov
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