Showing papers by "Bernard P. Zeigler published in 2017"

Combining DEVS and model-checking: concepts and tools for integrating simulation and analysis

Abstract: Our objectives here are to discuss the development of a formal framework that exploits the advantages of the discrete event system specification (DEVS) formalism and builds upon recent extensive work on verification combining DEVS and model checking for hybrid systems. DEVS offers the ability, via mathematical transformations called system morphisms, to map a system expressed in a formalism suitable for analysis (e.g., timed automata or hybrid automata) into the DEVS formalism for the purpose of simulation. We discuss a probabilistic extension of the FD-DEVS formalism that enables a set of model classes and tools derived from Markov-type models. The MS4 modelling environment provides a suite of tools that support this extension, called FP-DEVS. In this paper, we describe these tools and the concepts underlying them. We also provide examples of application of these concepts and discuss the open opportunities for research in this direction.

Using the Parallel DEVS Protocol for General Robust Simulation with Near Optimal Performance

Abstract: The Discrete Event Systems Specification (DEVS) formalism has been widely disseminated in this magazine and elsewhere for its applicability to computational science and engineering. While research in parallel and distributed simulation has been active in the past several decades, the utility of many techniques for high-performance DEVS simulation has been limited. Recent research has shown that that a reconstruction of Amdahl's and Gustafson's laws for parallelizing sequential code can afford a better understanding of the underlying principles and their application to simulation of DEVS models. In this article, the author shows, in comparison to the ideal case, that a relatively simple protocol based on the DEVS abstract simulator is both generally applicable and able to achieve significant speedup in parallel distributed simulation.

Hierarchical markov decision process based on devs formalism

Abstract: Markov decision processes (MDPs) have proven useful as models of stochastic planning and decision problems. To try to propose practical implementation of MDPs, hierarchical methods are often used in MDPs or reinforcement learning to delegate the optimization of the total problem to simpler hierarchical sub-problems. The goal of the paper is to propose a generic discrete-event based software Framework allowing to use hierarchical MDPs and reinforcement learning to solve planning or decision problems. The proposed approach has been validated using the "grid world" typical MDP use case.

Constructing and evaluating multi-resolution model pairs: an attrition modeling example

Abstract: Our aim is to illustrate how to apply the methodology for characterizing the useful approximation zone of a lumping process, as described in an earlier note in this journal. Here we review the procedure steps and then go on to present an example in the combat modeling area. We conclude by discussing implications for approximate morphism support of multi-resolution modeling.

System entity structure and model base framework in model based engineering of simulations for technical systems

Abstract: Model-based engineering is defined as the pragmatic utilization of model-based practices, namely, modeling, metamodeling and model transformations in various steps of engineering. In the last decade, the simulation of technical systems has leveraged graphical modeling and model-to-text transformations, but metamodeling and model transformation practices have not become widely accessible. Thereby, the benefits of employed model-driven approaches have been limited. System Entity Structures are directed labelled graphs that were proposed in the 1980s for specifying a family of system configurations for modular and hierarchical systems. The System Entity Structure and Model Base (SES/MB) framework combines the SES ontology with the classical workflow of modeling for an interactive or automatic generation of executable simulation models. After reviewing comparable approaches in software engineering, this paper discusses the application of SES/MB framework for metamodeling and model transformations for model-based engineering of simulations of technical systems.

Why should we develop simulation models in pairs

Abstract: The conventional approach to model construction for simulation is to focus on a single model and follow a more or less structured development cycle. Why we put in twice the time and effort to develop two models rather than one? The answer lies in the fact that like most greedy heuristics, short-sightedness at the beginning may be much more costly in the end. This talk will champion the cause of the pairs-of-models (perhaps families of models) with discussion of multiresolution modeling. We show how the pair-of-models approach leads to be better results overall than construction of a complex model followed by a simpler model developed subsequently by necessity under stress when complexity overwhelms. Benefits include the ability to perform mutual cross-calibration, avoiding the usual difficulties in harmonization of the underlying ontologies as well as ability to better reconcile and correlate predictions of referent system outcomes.

The state of innovation in modeling and simulation: the last 50 years

Abstract: Innovation in Modeling and Simulation (M&S) refers to exploiting new ideas, exploiting new technology, and employing out-of-the-box thinking, which lead to the creation of new methodologies, techniques, concepts, frameworks, and software. This paper addresses the following questions: (a) what was the state of the art in M&S 50 years ago, what is it today, how much progress has been made? (b) how much innovation in M&S has been accomplished over the last half a century? (c) what were the obstacles to innovation in M&S? (d) what are some recommendations to promote innovation in M&S? (e) what message should be sent to the funding agencies to encourage innovation in M&S?

DEv-PROMELA: an extension of PROMELA for the modelling, simulation and verification of discrete-event systems

Abstract: PROMELA is a well-known formalism for the modelling and the verification of concurrent systems. PROMELA deals with high-level specifications. As a result, PROMELA models are expressed in a high-level abstraction which does not consider explicit representation of time or events for example. But, the efficiency of the processes of verification and validation relies on the accuracy of the models. That is why we propose, in this paper, work to develop a new extension of PROMELA for the modelling of discrete-event systems. The verification of these models is then done by combining formal verification and simulation-based verification using SPIN and the tool DEv-PROMELA Studio, or using any existing DEVS simulators.

Emergence of human language: a DEVS-based systems approach

Abstract: Active-passive compositions provide a class of systems from which we can draw intuition and examples for generalizations about system emergence at the fundamental level. In this paper, we will employ the modeling and simulation framework based on system theory focusing on its concepts of iterative specification and the Discrete Event Systems Specification (DEVS) formalism. Our approach to emergence of human language formulates the problem as an instance of a set of components that must be coupled to form a system with innovative inter-component information exchange. We show that passive/active alternation interaction of a cross-coupled pair can satisfy the conditions required for well-defined systems.

Model Development and Execution Process with Repositories, Validation, and Verification

Abstract: Model Development and Execution Process with Repositories, Validation, and Verification In this chapter, we discuss another modeling and simulation environment that supports both development and storage of families of DEVS models for Systems of Systems. Component-based System Modeler (CoSMo) is grounded in a unified logical, persistence, and visual model development concept. We show how you can develop; store, retrieve, and instantiate DEVS SoS models such as those for service oriented and cloud systems (Chaps. 14 and 15). We develop a unified concept supporting logical, visual, and persistence modeling and simulation framework (CoSMoS) which lends itself for data (XML Schema) modeling and XML Schema code as well as cellular automata modeling. We show how the concept of SW/HW co-design for systems of systems fits seamlessly into the CoSMoS framework. While being based on the same underlying concepts of DEVS model construction, MS4 Me™ and CoSMoS offer somewhat different perspectives on support for SoS model construction.

Generic architecture for interactive mobile simulation of parallel devs models: a missile defense application

Abstract: Modeling and simulation (M&S) is a discipline oriented towards engineering and research, but it tends since the very last years to be used more and more by users and developers of mobile applications through cloud computing and web services. The M&S new tools involve mobile terminals (smartphone, tablet, etc.) exchanging data quantities increasingly important from sensors with an increasing transmission speed. This paper presents a generic approach (the DEVSimPy-mob mobile application) which aims to simulate models described with the DEVS formalism (Discrete EVent system Specification). DEVSimPy-mob communicates with a web REST (Representational State Transfer) server that delivers a set of web services dedicated to the simulation of DEVS models. A real case application stemming from Balistic Missile Defense simulations is presented to show how DEVSimPy-mob can be used to launch simulations from a mobile device, interact during the simulation process and visualize results.

Automated and Rule-Based Pruning and Experimental Execution

Abstract: The main features of the System Entity Structure, its specializations and aspects, as well as pruning and model generation have now been introduced. Such concepts provide a wealth and variety of potential hierarchical structures with which to tackle complex Systems of Systems problems. However, the rapidly growing combinatorial spaces that are set up by specialization and aspect selections can outstrip human capacity to do manual pruning. Accordingly, this chapter discusses automated pruning—concepts and tools for pruning that can reduce, and sometimes, eliminate, the manual pruning that is otherwise required. Enumerative pruning entirely eliminates manual pruning entirely but is restricted to small enough solution spaces. Random pruning samples from a large solution space to give a statistical picture of the space. Context-free and context-sensitive selection rules provide the ability to constrain the solution space to combinations that are more likely to meet your requirements. To conclude this chapter, we discuss a methodology and supporting concepts to create SES-based execution control of simulation models that lends itself to implementation on sequential computers as well as parallel and distributed platforms.

DEVS Support for Markov Modeling and Simulation

Abstract: Markov Modeling is among the most commonly used forms of model expression. Besides their general usefulness, the Markov concepts of stochastic modeling are implicitly at the heart of most forms of discrete-event simulation. This chapter, an addition to the second edition, shows how such concepts are fully compatible with the DEVS characterization of discrete-event models and a natural basis for the extended and integrated Markov Modeling facility developed within the MS4 Me environment. The facility described here offers an easy-to-use set of tools to develop Markov models which are full-fledged DEVS models and able to be integrated with other DEVS models just like other DEVS models. Due to their transition structure, Markov models can be individualized with specific transition probabilities/rates which can be changed during model execution for dynamic structural change. Finally, we discuss case studies where such modeling can provide significant insights through multi-resolution modeling in drug therapy and how speedup using parallel processing is limited by the interprocessor connection network. An appendix gives some background for this chapter on exponential distributions, Poisson processes, and Markov basics.