Inference of Event-Recording Automata Using Timed Decision Trees

Abstract

In regular inference, the problem is to infer a regular language, typically represented by a deterministic finite automaton (DFA) from answers to a finite set of membership queries, each of which asks whether the language contains a certain word. There are many algorithms for learning DFAs, the most well-known being the Ä £ algorithm due to Dana Angluin.However, there are almost no extensions of these algorithms to the setting of timed systems. We present an algorithm for inferring a model of a timed system using Angluin's setup. One of the most popular model for timed system is timed automata. Since timed automata can freely use an arbitrary number of clocks, we restrict our attention to systems that can be described by event-recording automata (DERAs). In previous work, we have presented an algorithm for inferring a DERA in the form of a region graph. In this paper, we present a novel inference algorithm for DERAs, which avoids constructing a (usually prohibitively large) region graph. We must then develop techniques for inferring guards on transitions of a DERA. Our construction deviates from previous work on inference of DERAs in that it first constructs a so called timed decision tree from observations of system behavior, which is thereafter folded into an automaton.

Improving active Mealy machine learning for protocol conformance testing

“…DFAs and Mealy machines are simple models and in some cases they will not be able to represent or identify all the complex behaviors of a software system. Some more powerful models with learning algorithms include: non-deterministic automata [60,29], probabilistic automata [19,17], Petri-nets [56], timed automata [57,35], I/O automata [5], and Büchi automata [39]. Despite their limited power, DFA and Mealy machine learning methods have recently been applied successfully to learn different types of complex systems such as web-services [15], X11 windowing programs [7], network protocols [24,9,22], and Java programs [59,26,46].…”

Machine Learning for Emergent Middleware

“…This basic pattern has been extended beyond the domain of learning DFAs to classes of automata better suited for modelling reactive systems in practice. On the basis of active learning algorithms for Mealy machines, inference algorithms for I/O-automata [1], timed automata [7], Petri Nets [6], and Register Automata [9], i.e., restricted flow graphs, have been developed.…”

Efficiently identifying deterministic real-time automata from labeled data

“…As mentioned in the introduction, the most closely related work deals with the problem of learning event recording automata (ERAs) (Grinchtein et al 2006). That work proposes an algorithm for learning these TAs from a timed teacher using membership and equivalence queries.…”

“…The most closely related study deals with the problem of learning event recording automata (ERAs), which is a restricted but still powerful class of TAs (Grinchtein et al 2006). Unfortunately, the proposed algorithm for the identification of ERAs requires an exponential amount of data in the worst case.…”