Towards Topologically Diverse Probabilistic Planning Benchmarks
Monday, 15 Jul 2024·
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Jaël Champagne Gareau
Éric Beaudry
Vladimir Makarenkov
Abstract
Markov Decision Processes (MDPs) are often used in Artificial Intelligence to
solve probabilistic sequential decision-making problems. In the last decades,
many probabilistic planning algorithms have been developed to solve MDPs.
However, the lack of standardized benchmarks makes it difficult to compare the
performance of these algorithms in different contexts. In this paper, we
identify important topological properties of MDPs that can make a significant
impact on the relative performance of probabilistic planning algorithms. We
also propose a new approach to generate synthetic MDP domains having different
topological properties. This approach relies on the connection between MDPs
and graphs and allows every graph generation technique to be used to generate
synthetic MDP domains.
Type
Publication
Proceedings of the International Federation of Classification Societies Conference
Authors
Postdoctoral Researcher in Computer Science
I am currently a postdoctoral researcher in computer science at Université
TÉLUQ, where my research focuses on speeding up the conversion of integer and
floating-point numbers into decimal strings. During my doctoral studies, I
designed algorithms and data structures that leverage modern computer
architectures to solve large instances of Markov decision processes (MDPs). In
my master’s research, I developed routing algorithms for electric vehicles
aimed at determining the optimal path between two points while minimizing
travel time (including driving, charging, and expected waiting time at
charging stations).
Authors
Authors