Dynamic State Space Partitioning for Adaptive Simulation Algorithms

Journal Title: EAI Endorsed Transactions on Collaborative Computing - Year 2016, Vol 2, Issue 10


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.

Authors and Affiliations

Tobias Helms, Steffen Mentel, Adelinde Uhrmacher


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  • EP ID EP45726
  • DOI http://dx.doi.org/10.4108/eai.14-12-2015.2262710
  • Views 189
  • Downloads 0

How To Cite

Tobias Helms, Steffen Mentel, Adelinde Uhrmacher (2016). Dynamic State Space Partitioning for Adaptive Simulation Algorithms. EAI Endorsed Transactions on Collaborative Computing, 2(10), -. https://europub.co.uk/articles/-A-45726