Mean-based Heuristic Search for Real-Time Planning
  In this paper, we introduce a new heuristic search algorithm based on mean
values for real-time 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 a real-time planner in the context of classical planning. MHSP is
evaluated on different planning problems and compared to existing algorithms
performing on-line search and learning. Besides, our results highlight the
capacity of MHSP to return plans in a real-time manner which tend to an optimal
plan over the time which is faster and of better quality compared to existing
algorithms in the literature.
