Completing and adapting models of biological processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

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

Completing and adapting models of biological processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

A Robust Class of Data Languages and an Application to Learning

We introduce session automata, an automata model to process data words, i.e., words over an infinite alphabet. Session automata support the notion of fresh data values, which are well suited for modeling protocols in which sessions using fresh values are of major interest, like in security protocols or ad-hoc networks. Session automata have an expressiveness partly extending, partly reducing that of classical register automata. We show that, unlike register automata and their various extensions, session automata are robust: They (i) are closed under intersection, union, and (resource-sensitive) complementation, (ii) admit a symbolic regular representation, (iii) have a decidable inclusion problem (unlike register automata), and (iv) enjoy logical characterizations. Using these results, we establish a learning algorithm to infer session automata through membership and equivalence queries

Choreography and Orchestration Conformance for System Design

Abstract. In a previous work we have presented a formal framework devoted to show the relevance of choreography and orchestration in the design of service oriented applications. Even if useful to start a formal investigation of the relationship between choreography and orchestration, the proposed framework was not suitable to specify real case studies. In fact, it simply permitted to specify all possible computations abstracting away from the conditions driving the choice of the actual behaviour. In this paper we tackle this problem by introducing the notion of state variables. The addition of state requires a substantial modification of the entire framework because the same state variable, at the level of choreography, can be actually stored in distributed orchestrators that will need to synchronize in order to maintain consistent views. In order to faithfully investigate this problem we also need to modify the formal model at the orchestration level, moving from synchronous to asynchronous communication as the latter is the communication modality of the ordinary communication infrastructures.

Model learning and test generation using cover automata

We propose an approach which, given a state-transition model of a system, constructs, in parallel, an approximate automaton model and a test suite for the system. The approximate model construction relies on a variant of Angluin's automata learning algorithm, adapted to finite cover automata. A finite cover automaton represents an approximation of the system which only considers sequences of length up to an established upper bound . Crucially, the size of the cover automaton, which normally depends on , can be significantly lower than the size of the exact automaton model. Thus, controlling , the state explosion problem normally associated with constructing and checking state based models can be mitigated. The proposed approach also allows for a gradual construction of the model and of the associated test suite, with complexity and time savings. Moreover, we provide automation of counterexample search, by a combination of black-box and random testing, and metrics to evaluate the quality of the produced results. The approach is presented and implemented in the context of the Event-B modeling language, but its underlying ideas and principles are much more general and can be applied to any system whose behavior can be suitably described by a state-transition model