Dynamic state space partitioning for external memory state space exploration

Dynamic State Space Partitioning for External Memory Model Checking

We describe a dynamic partitioning scheme usable by model checking techniques that divide the state space into partitions, such as most external memory and distributed model checking algorithms. The goal of the scheme is to reduce the number of transitions that link states belonging to different partitions, and thereby limit the amount of disk access and network communication. We report on seve...

Dynamic State-Space Partitioning in External-Memory Graph Search

State-of-the-art external-memory graph search algorithms rely on a hash function, or equivalently, a state-space projection function, that partitions the stored nodes of the state-space search graph into groups of nodes that are stored as separate files on disk. The scalability and efficiency of the search depends on properties of the partition: whether the number of unique nodes in a file alwa...

Predictive State Representations with State Space Partitioning

Predictive state representations (PSRs) are powerful methods of modeling dynamical systems by representing state through observational data. Most of the current PSR techniques focus on learning a complete PSR model from the entire state space. Consequently, the techniques are often not scalable due to the dimensional curse, which limits applications of PSR. In this paper, we propose a new PSR l...

Dynamic State Space Partitioning for Adaptive Simulation Algorithms

Adaptive simulation algorithms can automatically change their configuration during runtime to adapt to changing computational demands of a simulation, e.g., triggered by a changing number of model entities or the execution of a rare event. These algorithms can improve the performance of simulations. They can also reduce the configuration effort of the user. By using such algorithms with machine...

External memory algorithms for state space exploration in model checking and action planning