Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Regular Model Checking Using Inference of Regular Languages

Regular model checking is a method for verifying infinite-state systems based on coding their configurations as words over a finite alphabet, sets of configurations as finite automata, and transitions as finite transducers. We introduce a new general approach to regular model checking based on inference of regular languages. The method builds upon the observation that for infinite-state systems whose behaviour can be modelled using length-preserving transducers, there is a finite computation for obtaining all reachable configurations up to a certain length n. These configurations are a (positive) sample of the reachable configurations of the given system, whereas all other words up to length n are a negative sample. Then, methods of inference of regular languages can be used to generalize the sample to the full reachability set (or an overapproximation of it). We have implemented our method in a prototype tool which shows that our approach is competitive on a number of concrete examples. Furthermore, in contrast to all other existing regular model checking methods, termination is guaranteed in general for all systems with regular sets of reachable configurations. The method can be applied in a similar way to dealing with reachability relations instead of reachability sets too

Learning DFA for Simple Examples

We present a framework for learning DFA from simple examples. We show that efficient PAC learning of DFA is possible if the class of distributions is restricted to simple distributions where a teacher might choose examples based on the knowledge of the target concept. This answers an open research question posed in Pitt\u27s seminal paper: Are DFA\u27s PAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution? Our approach uses the RPNI algorithm for learning DFA from labeled examples. In particular, we describe an efficient learning algorithm for exact learning of the target DFA with high probability when a bound on the number of states (N) of the target DFA is known in advance. When N is not known, we show how this algorithm can be used for efficient PAC learning of DFAs

Inferencia gramatical para la detección de spam

El spam representa un problema de mala utilización de recursos técnicos y una molestia para los usuarios de correo electrónico. Tomando este problema como aplicación práctica, se pretende mostrar, con su justificación teórica, decisiones de diseño de un posible sistema inteligente destinado a controlarlo. El trabajo que se describe está actualmente en curso en el marco de un proyecto de investigación de la Universidad Nacional del Comahue.Eje: Inteligencia Computacional - MetaheurísticasRed de Universidades con Carreras en Informática (RedUNCI

Inferencia gramatical para la detección de spam

El spam representa un problema de mala utilización de recursos técnicos y una molestia para los usuarios de correo electrónico. Tomando este problema como aplicación práctica, se pretende mostrar, con su justificación teórica, decisiones de diseño de un posible sistema inteligente destinado a controlarlo. El trabajo que se describe está actualmente en curso en el marco de un proyecto de investigación de la Universidad Nacional del Comahue.Eje: Inteligencia Computacional - MetaheurísticasRed de Universidades con Carreras en Informática (RedUNCI

Computing and visualizing informative trajectories in temporaly annotated data

2n premi en la 3a edició dels Big Data Talent AwardsA trajectory in the medical world, is the sequence of events that occur during the life of a patient. In the recent years, these trajectories have been stored in the Electronical Health Records and many of the health organizations have databases with the clinical history of all their patients. The trajectories can be summarized in a trajectory graph which shows the different paths the trajectory of a patient may take. The graph contains events on its nodes and the edges contain the temporal relations. Previous works focused in the exploration of trajectory graphs only allow one event at each node, thus losing information and potentially mixing different groups of patients. In this work, we have developed a new procedure to extract the trajectory graphs that allows having several events in a single node of the graph. This procedure has been tested in two real world datasets: one related to diagnostics at hospital admissions, and the other on prescriptions in intensive care units.Award-winnin