Florian Geißer

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Abstraction 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(More)
Supporting state-dependent action costs in planning admits a more compact representation of many tasks. We generalize the additive heuristic h 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 heuristic. This allows us to(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)
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)
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)
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)
General game playing is the research field of being able to play multiple different kinds of games with one AI. We present Eager Beaver, a general game player based on Propositional Networks with dynamic code generation and an enhanced Upper Confidence Bounds applied to Trees (UCT) algorithm as an approach to solve this problem. We ran an evaluation study(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)
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