Aproximation Properties of Planning Benchmarks
نویسندگان
چکیده
For many classical planning domains, the computational complexity of non-optimal and optimal planning is known. However, little is known about the area in between the two extremes of finding some plan and finding optimal plans. In this contribution, we provide a complete classification of the propositional domains from the first four International Planning Competitions with respect to the approximation classes PO, PTAS, APX, poly-APX, and NPO.
منابع مشابه
Local Search Topology in Planning Benchmarks: An Empirical Analysis
Many state-of-the-art heuristic planners derive their heuristic function by relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. Looking at a collection of planning benchmarks, we measure topological properties of state spaces with respect to that relaxation. The results suggest that, given the heuristic based on the relaxation, many planning be...
متن کاملEngineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4
In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the determini...
متن کاملConstruction of Benchmarks for Comparison of Grid Resource Planning Algorithms
The present contribution will focus on the systematic construction of benchmarks used for the evaluation of resource planning systems. Two characteristics for assessing the complexity of the benchmarks were developed. These benchmarks were used to evaluate the resource management system GORBA and the optimization strategies for resource planning applied in this system. At first, major aspects o...
متن کاملSparse sampling heuristic search
In the article A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes, Kearns et al show that there’re now theoratical boundary’s to solve an mdp with an infinite state space in a such a way that the running time has no dependency’s on the size of the statespace. To come to this conclusion they introduce an algorithm that uses a sparse look-a-head tree to selec...
متن کاملOn Benchmarks for Combined Task and Motion Planning
Planning systems developed for Combined Task and Motion Planning (CTAMP) problems are most of the time evaluated using their own benchmarks. As a direct consequence, no comparison of these systems is currently possible, and as a side effect, there is a risk for planners to overspecialize. We compare several CTAMP benchmarks from three different perspectives (logical, geometric, dependency). In ...
متن کامل