Model Identification and Synthesis of Discrete-Event Systems

This chapter focuses on two important and tightly related problems, namely the identification and synthesis of discrete-event systems. Particular attention is devoted to two main formalisms in this area, i.e., finite state automata and Petri nets. The goal of this chapter is to provide a collection of references in this framework, and discuss the main research areas where such problems have been investigated. Due to the extensive literature, only some of the results are discussed in a certain detail, such as the basic ideas related to the theory of regions and the synthesis of labeled Petri nets, while other results are simply mentioned and the reader is addressed to the specific contributions for more details.

[1]  Philippe Darondeau,et al.  Deriving Unbounded Petri Nets from Formal Languages , 1998, CONCUR.

[2]  O. Nelles Nonlinear System Identification , 2001 .

[3]  Boudewijn F. van Dongen,et al.  ProM 4.0: Comprehensive Support for Real Process Analysis , 2007, ICATPN.

[4]  Jerome A. Feldman,et al.  On the Synthesis of Finite-State Machines from Samples of Their Behavior , 1972, IEEE Transactions on Computers.

[5]  Alessandro Giua,et al.  Identification of Petri Nets from Knowledge of Their Language , 2007, Discret. Event Dyn. Syst..

[6]  Sebastian Mauser,et al.  How to synthesize nets from languages - a survey , 2007, 2007 Winter Simulation Conference.

[7]  Wil M. P. van der Aalst,et al.  Process Mining: Discovering Direct Successors in Process Logs , 2002, Discovery Science.

[8]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Iickho Song,et al.  Identification of Finite State Automata With a Class of Recurrent Neural Networks , 2010, IEEE Transactions on Neural Networks.

[10]  David Lee,et al.  Principles and methods of testing finite state machines-a survey , 1996, Proc. IEEE.

[11]  Ernesto López-Mellado,et al.  A Comparative Analysis of Recent Identification Approaches for Discrete-Event Systems , 2010 .

[12]  Javier Esparza,et al.  Learning Workflow Petri Nets , 2010, Fundam. Informaticae.

[13]  Alessandro Giua,et al.  Linear programming techniques for the identification of place/transition nets , 2008, 2008 47th IEEE Conference on Decision and Control.

[14]  Wil M.P. van der Aalst,et al.  Rediscovering workflow models from event-based data , 2001 .

[15]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[16]  van der Wmp Wil Aalst,et al.  Workflow mining: which processes can be rediscovered? , 2002 .

[17]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[18]  Wil M.P. van der Aalst,et al.  Process mining: discovering workflow models from event-based data , 2001 .

[19]  Tadao Murata Synthesis of Decision-Free Concurrent Systems for Prescribed Resources and Performance , 1980, IEEE Transactions on Software Engineering.

[20]  E. Mark Gold,et al.  Complexity of Automaton Identification from Given Data , 1978, Inf. Control..

[21]  Learning languages from positive data and a finite number of queries , 2006, Inf. Comput..

[22]  Robert Lorenz,et al.  Towards Synthesis of Petri Nets from Scenarios , 2006, ICATPN.

[23]  Nidhal Rezg,et al.  Design of a live and maximally permissive Petri net controller using the theory of regions , 2003, IEEE Trans. Robotics Autom..

[24]  A. Nerode,et al.  Linear automaton transformations , 1958 .

[25]  E. Lopez-Mellado,et al.  Required event sequences for identification of Discrete Event Systems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[26]  V. A. Kozlovskii,et al.  Analysis and synthesis of abstract automata , 2010 .

[27]  John Case,et al.  Language Learning with Some Negative Information , 1993, Journal of computer and system sciences (Print).

[28]  Boudewijn F. van Dongen,et al.  Process Discovery using Integer Linear Programming , 2009, Fundamenta Informaticae.

[29]  Kwang Y. Lee,et al.  Diagonal recurrent neural networks for dynamic systems control , 1995, IEEE Trans. Neural Networks.

[30]  Lingxi Li,et al.  Least-Cost Firing Sequence Estimation in Labeled Petri Nets with Unobservable Transitions , 2007, 2007 American Control Conference.

[31]  Peter Graubmann The Construction of EN Systems from a Given Trace Behavior , 1987, European Workshop on Applications and Theory of Petri Nets.

[32]  C.N. Hadjicostis,et al.  State Estimation in Discrete Event Systems Modeled by Labeled Petri Nets , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[33]  Daniel N. Osherson,et al.  Criteria of Language Learning , 1982, Inf. Control..

[34]  Dana Angluin,et al.  Inductive Inference of Formal Languages from Positive Data , 1980, Inf. Control..

[35]  Kunihiko Hiraishi Construction of a Class of Safe Petri Nets by Presenting Firing Sequences , 1992, Application and Theory of Petri Nets.

[36]  Robin Bergenthum,et al.  Process Mining Based on Regions of Languages , 2007, BPM.

[37]  Michael C. Mozer,et al.  Dynamic On-line Clustering and State Extraction: An Approach to Symbolic Learning , 1998, Neural Networks.

[38]  Taylor L. Booth,et al.  Sequential machines and automata theory , 1967 .

[39]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.

[40]  L. P. J. Veelenturf,et al.  AN AUTOMATA-THEORETICAL APPROACH TO DEVELOPING LEARNING NEURAL NETWORKS , 1981 .

[41]  Luciano Lavagno,et al.  Deriving Petri Nets for Finite Transition Systems , 1998, IEEE Trans. Computers.

[42]  Philippe Darondeau,et al.  Polynomial Algorithms for the Synthesis of Bounded Nets , 1995, TAPSOFT.

[43]  Mariagrazia Dotoli,et al.  Real time identification of discrete event systems using Petri nets , 2008, Autom..

[44]  John Case,et al.  Machine Inductive Inference and Language Identification , 1982, ICALP.

[45]  Ramavarapu S. Sreenivas On minimal representations of Petri net languages , 2006, IEEE Transactions on Automatic Control.

[46]  Josep Carmona,et al.  A Region-Based Algorithm for Discovering Petri Nets from Event Logs , 2008, BPM.

[47]  Padhraic Smyth,et al.  Learning Finite State Machines With Self-Clustering Recurrent Networks , 1993, Neural Computation.

[48]  E. Lopez-Mellado,et al.  Incremental synthesis of Petri net models for identification of discrete event systems , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[49]  Marc Richetin,et al.  Identification of automata by sequential learning , 1984, Pattern Recognit. Lett..

[50]  L. P. J. Veelenturf,et al.  Inference of Sequential Machines from Sample Computations , 1978, IEEE Transactions on Computers.

[51]  Philippe Darondeau,et al.  Theory of Regions , 1996, Petri Nets.

[52]  Luciano Lavagno,et al.  A Symbolic Algorithm for the Synthesis of Bounded Petri Nets , 2008, Petri Nets.

[53]  Andrej Dobnikar,et al.  On-line identification and reconstruction of finite automata with generalized recurrent neural networks , 2003, Neural Networks.

[54]  Manuel Silva Suárez,et al.  Top-down synthesis of live and bounded free choice nets , 1990, Applications and Theory of Petri Nets.

[55]  Robin Bergenthum,et al.  Synthesis of Petri nets from infinite partial languages , 2007, 2008 8th International Conference on Application of Concurrency to System Design.

[56]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[57]  Jehuda Kella Sequential Machine Identification , 1971, IEEE Transactions on Computers.

[58]  Boudewijn F. van Dongen,et al.  Workflow mining: A survey of issues and approaches , 2003, Data Knowl. Eng..

[59]  Boudewijn F. van Dongen,et al.  Discovering Workflow Performance Models from Timed Logs , 2002, EDCIS.