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Sophisticated empirical methods drive the development of high-performance solvers for an increasing range of problems from industry and academia. However, automated tools implementing these methods are often difficult to develop and to use. We address this issue with two contributions. First, we develop a formal description of meta-algorithmic problems and(More)
In this work, we present the FD-Autotune learning planning system, which is based on the idea of domain-specific configuration of the latest, highly parametric version of the Fast Downward Planning Framework by means of a generic automated algorithm configuration procedure. We describe how the extremely large configuration space of Fast Downward was(More)
When designing state-of-the-art, domain-independent planning systems , many decisions have to be made with respect to the domain analysis or compilation performed during preprocessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting(More)
We give a short description of the solver that ranked third in Track Two of the International Timetabling Competition 2007 (ITC2007). It implements a heuristic approach based on stochastic local search and consists of several modules that were found to be useful in different phases of the solution process. Common to all modules is the consideration of only(More)
State-of-the-art planners often exhibit substantial runtime variation , making it useful to be able to efficiently predict how long a given planner will take to run on a given instance. In other areas of AI, such needs are met by building so-called empirical performance models (EPMs), statistical models derived from sets of problem instances and performance(More)
Modern solvers for hard computational problems often expose parameters that permit customization for high performance on specific instance types. Since it is tedious and time-consuming to manually optimize such highly parameterized algorithms, recent work in the AI literature has developed automated approaches for this algorithm configuration problem(More)
Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated algorithm configuration tools, and consequently often create solvers with many parameters and vast configuration spaces. However, there has been very little work to help these algorithm developers answer questions about the high-quality(More)
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during prepro-cessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting(More)
Timetabling tasks form a widely studied type of resource scheduling problem, with important real-world applications in schools, universities and other educational settings. In this work, we focus on post-enrollment course timetabling, the problem that was covered by Track 2 of the recent 2nd International Timetabling Competition (ITC2007). Following an(More)
When designing state-of-the-art, domain-independent planning systems, many decisions have to be made with respect to the domain analysis or compilation performed during prepro-cessing, the heuristic functions used during search, and other features of the search algorithm. These design decisions can have a large impact on the performance of the resulting(More)