Florian Geißer

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Supporting state-dependent action costs in planning admits a more compact representation of many tasks. We generalize the additive heuristic h add and compute it by embedding decision-diagram representations of action cost functions into the RPG. We give a theoretical evaluation and present an implementation of the generalized h add heuristic. This allows(More)
ion heuristics are a popular method to guide optimal search algorithms in classical planning. Cost partitionings allow to sum heuristic estimates admissibly by distributing action costs among the heuristics. We introduce statedependent cost partitionings which take context information of actions into account, and show that an optimal state-dependent cost(More)
We introduce the MDP-Evaluation Stopping Problem, the optimization problem faced by participants of the International Probabilistic Planning Competition 2014 that focus on their own performance. It can be constructed as a meta-MDP where actions correspond to the application of a policy on a base-MDP, which is intractable in practice. Our theoretical(More)
ion heuristics are a popular method to guide optimal search algorithms in classical planning. Cost partitionings allow to sum heuris-tic estimates admissibly by distributing action costs among the heuristics. We introduce state-dependent cost partitionings which take context information of actions into account, and show that an optimal state-dependent cost(More)
In General Game Playing, a player receives the rules of an unknown game and attempts to maximize his expected reward. Since 2011, the GDL-II rule language extension allows the formulation of nondeterministic and partially observable games. In this paper , we present an algorithm for such games, with a focus on the single-player case. Conceptually, at each(More)
Localization in dynamic environments is still a challenging problem in robotics - especially if rapid and large changes occur irregularly. Inspired by SLAM algorithms, our Bayesian approach to this so-called dynamic localization problem divides it into a localization problem and a mapping problem, respectively. To tackle the localization problem we use a(More)
Abstraction heuristics are a popular method to guide optimal search algorithms in classical planning. Cost partitionings allow to sum heuristic estimates admissibly by partitioning action costs among the abstractions. We introduce state-dependent cost partitionings which take context information of actions into account, and show that an optimal(More)
When planning for tasks that feature both state-dependent action costs and conditional effects using relaxation heuristics, the following problem appears: handling costs and effects separately leads to worse-than-necessary heuristic values, since we may get the more useful effect at the lower cost by choosing different values of a relaxed variable when(More)
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