Event logs are often one of the main sources information to understand behavior a system. While numerous approaches have extracted partial from event logs, in this work, we aim at inferring global model system its logs. We consider real-time systems, which can be modeled with Timed Automata: our approach is thus Automata learner. There handful related however, they might require lot parameters ...

In this paper, we describe FACS, a new Michigan style architecture able to build Finite-State Automata controllers for agents learning to solve nonMarkov problems. FACS relies on a population of partial automata and implements a Reinforcement Learning algorithm to compute the strength of each automaton and a Genetic Algorithm to select and discover efficient automata. We detail our approach and...

This paper introduces NL, a learning algorithm for inferring non-deterministic finite-state automata using membership and equivalence queries. More specifically, residual finite-state automata (RFSA) are learned similar as in Angluin’s popular L algorithm, which however learns deterministic finite-state automata (DFA). As RFSA can be exponentially more succinct than DFA, RFSA are the preferable...

breast cancer continues to be a significant public health problem in the world. early detection is the key for improving breast cancer prognosis. in this way, magnetic resonance imaging (mri) is emerging as a powerful tool for the detection of breast cancer. breast mri presently has two major challenges. first, its specificity is relatively poor, and it detects many false positives (fps). secon...