We introduce a new statistical relational learning (SRL) approach in which models for structured data, especially network data, are constructed as networks of communicating finite probabilistic automata. Leveraging existing automata learning methods from the area of grammatical inference, we can learn generic models for network entities in the form of automata templates. As is characteristic fo...

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...

In this paper, we introduce a novel framework of cellular automata based computing that is capable of long short-term memory. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution...

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...