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

Active learning of interface programs

Computer systems today are no longer monolithic programs; instead they usually comprise multiple interacting programs. With the continuous growth of these systems and with their integration into systems of systems, interoperability becomes a fundamental issue. Integration of systems is more complex and occurs more frequently than ever before. One solution to this problem could be the automated model-based synthesis of mediators at runtime. However, this approach has strong prerequisites. It requires the existence of adequate models of the systems to be connected. Many systems encountered in practice, on the other hand, do not come with models. In such cases models have to be constructed ex post (at runtime). Furthermore, adequate models must capture control as well as data aspects of a system. In most protocols, for instance, data parameters (e.g., session identifiers or sequence numbers) can influence system behavior. Models of such systems can be thought of as interface programs: Rather than covering only the control behavior, they describe explicitly which data values are relevant to the communication and have to be remembered and reused. This thesis addresses the problem of inferring interface programs of systems at runtime using active automata learning techniques. Active automata learning uses a test-based and counterexample-driven approach to inferring models of black-box systems. The method has originally been introduced for finite automata (the popular L* algorithm). Extending active learning to interface programs requires research in three directions: First, the efficiency of active learning algorithms has to be optimized to scale when dealing with data parameters. Second, techniques are needed for finding counterexamples driving the learning process in practice. Third, active learning has to be extended to richer models than Mealy machines or DFAs, capable of expressing interface programs. The work presented in this thesis improves the state of the art in all three directions. More concretely, the contributions of this thesis are the following: first, an efficient active learning algorithm for DFAs and Mealy machines that combines the ideas of several known active learning algorithms in a non-trivial way; second, a framework for finding counterexamples in black-box scenarios, leveraging the incremental and monotonic evolution of hypothetical models characteristic of active automata learning; third, and most importantly, the technically involved extension of the partition/refinement-based approach of active learning to interface programs. The impact of extending active learning to interface programs becomes apparent already for small systems. We inferred a simple data structure (a nested stack of overall capacity 16) as an interface program in no more than 20 seconds, using less than 45,000 tests and only 9 counterexamples. The corresponding Mealy machine model, on the other hand, would have more than 10 to the power of 9 states already in the case of a very small finite data domain of size 4 and require significantly more than 10 to the power of 9 tests when being inferred using the classic L* algorithm

Machine learning for emergent middleware

Highly dynamic and heterogeneous distributed systems are challenging today's middleware technologies. Existing middleware paradigms are unable to deliver on their most central promise, which is offering interoperability. In this paper, we argue for the need to dynamically synthesise distributed system infrastructures according to the current operating environment, thereby generating "Emergent Middleware'' to mediate interactions among heterogeneous networked systems that interact in an ad hoc way. The paper outlines the overall architecture of Enablers underlying Emergent Middleware, and in particular focuses on the key role of learning in supporting such a process, spanning statistical learning to infer the semantics of networked system functions and automata learning to extract the related behaviours of networked systems

Demonstrating Learning of Register Automata

International audienc

Allelopathy And Weed Competition

Currently, only two herbicides, Londax® (bensulfuron) and Taipan® (benzofenap) are available for the effective control of all four major broadleaf weeds infesting NSW rice paddocks. Prolonged and widespread use of these two herbicides in the rice growing regions increases the threat of herbicide resistance. The low likelihood of new herbicides in the foreseeable future increases the impact of herbicide resistance on the Australian rice industry. Allelopathy, chemical interactions between plants, is an alternative control option. Weeds could be controlled by using crops which have been developed to exert their own weed control by releasing chemicals into the soil. These naturally occurring compounds could play a valuable role in an integrated weed management system, potentially reducing the amount of synthetic herbicides required for weed control. In rice, the potential use of allelopathy in weed control has been explored by several researchers worldwide. Funding for work on allelopathic potential was provided by the Rice CRC as they recognised that the Australian weed community is very different and many of the weeds infesting rice paddocks are typically Australian problems not likely to be tackled by international research groups. Twenty-seven rice cultivars were examined in the laboratory for their allelopathic potential against several currently important and potentially important rice weeds in Australia, namely barnyard grass (Echinochloa crus-galli), dirty dora (Cyperus difformis), lance-leaved water plantain (Alisma lanceolatum), starfruit (Damasonium minus), arrowhead (Sagittaria montevidensis) and S. graminea. Weed root growth inhibition ranged from 0.3 % to 93.6 % of the control depending on the cultivar and the weed species being tested. One weed was actually stimulated by Langi. Several rice varieties significantly inhibited root growth of more than one weed. A field trial using starfruit as the test species was conducted to see if those cultivars which inhibited starfruit in the laboratory experiment also inhibited starfruit in the field and to determine whether allelopathy was an important factor in the resulting field performance. Twenty-four cultivars were used in a field trial based at the Yanco Agricultural Institute. Starfruit dry matter was measured as an indicator of weed inhibition. It was found that there was a correlation between laboratory and field results, and that allelopathy was an important contributor to field performance of a rice variety

VerifyThis 2015 A program verification competition

VerifyThis 2015 was a one-day program verification competition which took place on April 12th, 2015 in London, UK, as part of the European Joint Conferences on Theory and Practice of Software (ETAPS 2015). It was the fourth instalment in the VerifyThis competition series. This article provides an overview of the VerifyThis 2015 event, the challenges that were posed during the competition, and a high-level overview of the solutions to these challenges. It concludes with the results of the competition and some ideas and thoughts for future instalments of VerifyThis

Automated Learning Setups in Automata Learning

International audienc