Reversing place transition nets

Petri nets are a well-known model of concurrency and provide an ideal setting for the study of fundamental aspects in concurrent systems. Despite their simplicity, they still lack a satisfactory causally reversible semantics. We develop such semantics for Place/Transitions Petri nets (P/T nets) based on two observations. Firstly, a net that explicitly expresses causality and conflict among events, for example an occurrence net, can be straightforwardly reversed by adding a reverse transition for each of its forward transitions. Secondly, given a P/T net the standard unfolding construction associates with it an occurrence net that preserves all of its computation. Consequently, the reversible semantics of a P/T net can be obtained as the reversible semantics of its unfolding. We show that such reversible behaviour can be expressed as a finite net whose tokens are coloured by causal histories. Colours in our encoding resemble the causal memories that are typical in reversible process calculi.Fil: Melgratti, Hernan Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Mezzina, Claudio Antares. Università Degli Studi Di Urbino Carlo Bo; ItaliaFil: Ulidowski, And Irek. University of Leicester; Reino Unid

Reductions and Abstractions for Optimization of Modular Timed Automata

Time optimization of concurrent sequences of operations is in this paper solved by timed automata. To reduce the complexity of this classical problem, including applications such as planning and scheduling, an abstraction method has recently been proposed based on local optimization (Hagebring and Lennartson, 2019). In a modular subsystem, local paths without any communication with other subsystems are optimized with respect to time, and when subsystems are synchronized more local behavior appears. The proposed method has shown to be successful, drastically reducing computational complexity for important classes of planning problems. The only drawback is that the synchronous composition includes a heuristic non-standard synchronous composition procedure to achieve true con currency. In this paper a simple solution to this problem is presented based on the original synchronous composition of timed automata. In the transformation of the timed automaton to an ordinary automaton, where time weights are generated, it is first observed that the state space often increases dramatically in this transformation. To solve this complexity problem, an efficient reduction is proposed as a complement to local optimization, and both methods are demonstrated to be very efficient when they are applied to realistic benchmark examples. Copyright (C) 2022 The Authors

Partial Order Reduction for Reachability Games

Partial order reductions have been successfully applied to model checking of concurrent systems and practical applications of the technique show nontrivial reduction in the size of the explored state space. We present a theory of partial order reduction based on stubborn sets in the game-theoretical setting of 2-player games with reachability/safety objectives. Our stubborn reduction allows us to prune the interleaving behaviour of both players in the game, and we formally prove its correctness on the class of games played on general labelled transition systems. We then instantiate the framework to the class of weighted Petri net games with inhibitor arcs and provide its efficient implementation in the model checker TAPAAL. Finally, we evaluate our stubborn reduction on several case studies and demonstrate its efficiency

Discovering Hierarchical Process Models: an Approach Based on Events Clustering

Process mining is a field of computer science that deals with discovery and analysis of process models based on automatically generated event logs. Currently, many companies use this technology for optimization and improving their processes. However, a discovered process model may be too detailed, sophisticated and difficult for experts to understand. In this paper, we consider the problem of discovering a hierarchical business process model from a low-level event log, i.e., the problem of automatic synthesis of more readable and understandable process models based on information stored in event logs of information systems. Discovery of better structured and more readable process models is intensively studied in the frame of process mining research from different perspectives. In this paper, we present an algorithm for discovering hierarchical process models represented as two-level workflow nets. The algorithm is based on predefined event ilustering so that the cluster defines a sub-process corresponding to a high-level transition at the top level of the net. Unlike existing solutions, our algorithm does not impose restrictions on the process control flow and allows for concurrency and iteration