{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:29:16Z","timestamp":1780928956415,"version":"3.54.1"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Ltd","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advs. Complex Syst."],"published-print":{"date-parts":[[2011,10]]},"abstract":"<jats:p>This article introduces both a new algorithm for reconstructing epsilon-machines from data, as well as the decisional 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 some a priori knowledge 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.<\/jats:p>","DOI":"10.1142\/s0219525911003347","type":"journal-article","created":{"date-parts":[[2011,8,11]],"date-time":"2011-08-11T07:03:18Z","timestamp":1313046198000},"page":"761-794","source":"Crossref","is-referenced-by-count":10,"title":["RECONSTRUCTION OF EPSILON-MACHINES IN PREDICTIVE FRAMEWORKS AND DECISIONAL STATES"],"prefix":"10.1142","volume":"14","author":[{"given":"NICOLAS","family":"BRODU","sequence":"first","affiliation":[{"name":"University of Rennes 1, Rennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-4286-2"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1007\/s00354-008-0052-x"},{"key":"rf4","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1111\/j.2517-6161.1974.tb00999.x","volume":"36","author":"Besag J.","journal-title":"J. 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