18,274 research outputs found
Analysis of Petri Nets and Transition Systems
This paper describes a stand-alone, no-frills tool supporting the analysis of
(labelled) place/transition Petri nets and the synthesis of labelled transition
systems into Petri nets. It is implemented as a collection of independent,
dedicated algorithms which have been designed to operate modularly, portably,
extensibly, and efficiently.Comment: In Proceedings ICE 2015, arXiv:1508.0459
Feedback control logic synthesis for non safe Petri nets
This paper addresses the problem of forbidden states of non safe Petri Net
(PN) modelling discrete events systems. To prevent the forbidden states, it is
possible to use conditions or predicates associated with transitions.
Generally, there are many forbidden states, thus many complex conditions are
associated with the transitions. A new idea for computing predicates in non
safe Petri nets will be presented. Using this method, we can construct a
maximally permissive controller if it exists
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
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Analyzing safety and fault tolerance using time Petri nets
The application of time Petri net modelling and analysis techniques to safety-critical real-time systems is explored and procedures described which allow analysis of safety, recoverability, and fault tolerance. These procedures can be used to help determine software requirements, to guide the use of fault detection and recovery procedures, to determine conditions which require immediate miti gating action to prevent accidents, etc. Thus it is possible to establish important properties duing the synthesis of the system and software design instead of using guesswork and costly a posteriori analysis
Desynchronization: Synthesis of asynchronous circuits from synchronous specifications
Asynchronous implementation techniques, which measure logic delays at run time and activate registers accordingly, are inherently more robust than their synchronous counterparts, which estimate worst-case delays at design time, and constrain the clock cycle accordingly. De-synchronization is a new paradigm to automate the design of asynchronous circuits from synchronous specifications, thus permitting widespread adoption of asynchronicity, without requiring special design skills or tools. In this paper, we first of all study different protocols for de-synchronization and formally prove their correctness, using techniques originally developed for distributed deployment of synchronous language specifications. We also provide a taxonomy of existing protocols for asynchronous latch controllers, covering in particular the four-phase handshake protocols devised in the literature for micro-pipelines. We then propose a new controller which exhibits provably maximal concurrency, and analyze the performance of desynchronized circuits with respect to the original synchronous optimized implementation. We finally prove the feasibility and effectiveness of our approach, by showing its application to a set of real designs, including a complete implementation of the DLX microprocessor architectur
A Fuzzy Petri Nets Model for Computing With Words
Motivated by Zadeh's paradigm of computing with words rather than numbers,
several formal models of computing with words have recently been proposed.
These models are based on automata and thus are not well-suited for concurrent
computing. In this paper, we incorporate the well-known model of concurrent
computing, Petri nets, together with fuzzy set theory and thereby establish a
concurrency model of computing with words--fuzzy Petri nets for computing with
words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of
transitions are some special words modeled by fuzzy sets. By employing the
methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which
makes it possible for computing with more words. The language expressiveness of
the two formal models of computing with words, fuzzy automata for computing
with words and FPNCWs, is compared as well. A few small examples are provided
to illustrate the theoretical development.Comment: double columns 14 pages, 8 figure
Mean-Payoff Optimization in Continuous-Time Markov Chains with Parametric Alarms
Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events
that can be non-exponentially distributed. Within parametric ACTMCs, the
parameters of alarm-event distributions are not given explicitly and can be
subject of parameter synthesis. An algorithm solving the -optimal
parameter synthesis problem for parametric ACTMCs with long-run average
optimization objectives is presented. Our approach is based on reduction of the
problem to finding long-run average optimal strategies in semi-Markov decision
processes (semi-MDPs) and sufficient discretization of parameter (i.e., action)
space. Since the set of actions in the discretized semi-MDP can be very large,
a straightforward approach based on explicit action-space construction fails to
solve even simple instances of the problem. The presented algorithm uses an
enhanced policy iteration on symbolic representations of the action space. The
soundness of the algorithm is established for parametric ACTMCs with
alarm-event distributions satisfying four mild assumptions that are shown to
hold for uniform, Dirac and Weibull distributions in particular, but are
satisfied for many other distributions as well. An experimental implementation
shows that the symbolic technique substantially improves the efficiency of the
synthesis algorithm and allows to solve instances of realistic size.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
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