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Reconstruction Of Epsilon-Machines In Predictive Frameworks And Decisional States

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  • NICOLAS BRODU

    (University of Rennes 1, Rennes, France)

Abstract

This article introduces both a new algorithm for reconstructing epsilon-machines from data, as well as thedecisional states. These are defined as the internal states of a system that lead to the same decision, based on a user-provided utility or pay-off function. The utility function encodes somea prioriknowledge external to the system, it quantifies how bad it is to make mistakes. The intrinsic underlying structure of the system is modeled by an epsilon-machine and its causal states. The decisional states form a partition of the lower-level causal states that is defined according to the higher-level user's knowledge. In a complex systems perspective, the decisional states are thus the "emerging" patterns corresponding to the utility function. The transitions between these decisional states correspond to events that lead to a change of decision. The new REMAPF algorithm estimates both the epsilon-machine and the decisional states from data. Application examples are given for hidden model reconstruction, cellular automata filtering, and edge detection in images.

Suggested Citation

  • Nicolas Brodu, 2011. "Reconstruction Of Epsilon-Machines In Predictive Frameworks And Decisional States," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 14(05), pages 761-794.
    • Handle: RePEc:wsi:acsxxx:v:14:y:2011:i:05:n:s0219525911003347
    DOI: 10.1142/S0219525911003347
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    Cited by:

    1. Jaroslav Vítků & Petr Dluhoš & Joseph Davidson & Matěj Nikl & Simon Andersson & Přemysl Paška & Jan Šinkora & Petr Hlubuček & Martin Stránský & Martin Hyben & Martin Poliak & Jan Feyereisl & Marek Ros, 2020. "ToyArchitecture: Unsupervised learning of interpretable models of the environment," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-50, May.

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