Dynamic State Space Partitioning for Adaptive Simulation Algorithms

Helms, Tobias and Mentel, Steffen and Uhrmacher, Adelinde M. (2016) Dynamic State Space Partitioning for Adaptive Simulation Algorithms. In: 9th EAI International Conference on Performance Evaluation Methodologies and Tools, 14-16 Dec 2015, Berlin, Germany. Proceedings, published by ICST, Brussels, Belgium, pp. 149-152.

Full text not available from this repository.
Official URL: http://eudl.eu/doi/10.4108/eai.14-12-2015.2262710


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 learning techniques, the advantages come with a cost, i.e., the algorithm needs time to learn good adaptation policies and it must be equipped with the ability to observe its environment. An important challenge is to partition the observations to suitable macro states to improve the effectiveness and efficiency of the learning algorithm. Typically, aggregation algorithms, e.g., the adaptive vector quantization algorithm (AVQ), that dynamically partition the state space during runtime are preferred here. In this paper, we integrate the AVQ into an adaptive simulation algorithm.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN: 978-1-63190-096-9
Uncontrolled Keywords: adaptive algorithms reinforcement learning component-based simulation software dynamic state space representations
Projects: ESCeMMo