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02.04.2019 | Theoretical advances

On enhancing the deadlock-preventing object migration automaton using the pursuit paradigm

Zeitschrift:
Pattern Analysis and Applications
Autoren:
Abdolreza Shirvani, B. John Oommen
Wichtige Hinweise
The second author is grateful for the partial support provided by NSERC, the Natural Sciences and Engineering Research Council of Canada. The second author is also an Adjunct Professor at the University of Agder in Grimstad, Norway. A preliminary version of some of the results of this paper was presented at ICTCS’17, the International Conference on New Trends in Computing, Amman, Jordan, in October 2017. We are very grateful for the feedback from the anonymous Referees of the original submission. Their input significantly improved the quality of this final version.
B. J. Oommen: The author is also an Adjunct Professor with University of Agder, Grimstad, Norway.

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Abstract

Probably, the most reputed solution for partitioning, which has applications in databases, attribute partitioning, processor-based assignment and many other similar scenarios, is the object migration automata (OMA). However, one of the known deficiencies of the OMA is that when the problem size is large, i.e., the number of objects and partitions are large, the probability of receiving a reward, which “strengthens” the current partitioning, from the Environment is not significant. This is because of an internal deadlock scenario which is discussed in this paper. As a result of this, it can take the OMA a considerable number of iterations to recover from an inferior configuration. This property, which characterizes learning automaton (LA) in general, is especially true for the OMA-based methods. In spite of the fact that various solutions have been proposed to remedy this issue for general families of LA, overcoming this hurdle is a completely unexplored area of research for conceptualizing how the OMA should interact with the Environment. Indeed, the best reported version of the OMA, the enhanced OMA (EOMA), has been proposed to mitigate the consequent deadlock scenario. In this paper, we demonstrate that the incorporation of the intrinsic properties of the Environment into the OMA’s design leads to a higher learning capacity and to a more consistent partitioning. To achieve this, we incorporate the state-of-the-art pursuit principle utilized in the field of LA by estimating the Environment’s reward/penalty probabilities and using them to further augment the EOMA. We also verify the performance of our proposed method, referred to as the pursuit EOMA (PEOMA), through simulation, and demonstrate a significant increase in the convergence rate, i.e., by a factor of about forty. It also yields a noticeable reduction in sensitivity to the noise in the Environment. The paper also includes some results obtained for a real-world application domain involving faulty sensors.

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