Iterative Depth-First Search for Fully Observable Non-Deterministic Planning

Mattmüller et al. (2010) is a planner based on an adapted version of Hansen and Zilberstein (2001), a heuristic search algorithm that has theoretical guarantees to extract strong cyclic solutions for Markov decision problems. Kuter et al. (2008) makes use of Classical Planning algorithms to solve planning tasks. Fu et al. (2011) is similar to, but the main difference is that avoids exploring already explored/solved states, being more efficient than . Muise et al. (2012) is one the most efficient planners in the literature, and it is built upon some improvements over the state relevance techniques, such as avoiding dead-ends states. The main idea of these planners is selecting a reachable state s by the current policy that still is undefined in the current policy.