Showing papers by "Bernard P. Zeigler published in 1996"

A knowledge-based simulation environment for hierarchical flexible manufacturing

Abstract: This article presents an approach to embedding expert systems within an object oriented simulation environment. The basic idea is to create classes of expert system models that can be interfaced with other model classes. An expert system shell is developed within a knowledge-based design and simulation environment which combines artificial intelligence and systems modeling concepts. In the given framework, interruptible and distributed expert systems can be defined as components of simulations models. This facilitates simulation modeling of knowledge-based controls for flexible manufacturing and many other autonomous intelligent systems. Moreover, the structure of a system can be specified using a recursive system entity structure (SES) and unfolded to generate a family of hierarchical structures using an extension of SES pruning called recursive pruning. This recursive generation of hierarchical structures is especially appropriate for design of multilevel flexible factories. The article illustrates the utility of the proposed framework within the flexible manufacturing context.

DEVS-C++: a high performance modelling and simulation environment

Abstract: Simulation of landscape ecosystems with high realism demands computing power greatly exceeding that of curent workstation technology. However, the prospects are excellent that modelling and simulation environments may be implemented on next-generation high-performance, heterogeneous distributed computing platforms. Computing technology is becoming powerful enough to support the voluminous amounts of knowledge/information necessary for representing such systems and the speed required of simulations to provide reliable answers in reasonable time. This paper provides an overview of a project to develop a high-performance modelling and simulation environment to support modelling of large-scale, high-resolution landscape systems: DEVS-C++, an implementation of discrete event system specification (DEVS) using container classes with C++.

Design of high level modelling / high performance simulation environments

Abstract: Advances in massively parallel platforms are increasing the prospects for high performance discrete event simulation. Still the difficulty in parallel programming persists and there is increasing demand for high level support for building discrete event models to execute on such platforms. We present a parallel DEVS-based (Discrete Event System Specification) simulation environment that can execute on distributed memory multicomputer systems with benchmarking results of a class of high resolution, large scale ecosystem models. Underlying the environment is a parallel container class library for hiding the details of message passing technology while providing high level abstractions for hierarchical, modular DEVS models. The C++ implementation working on the Thinking Machines CM-5 demonstrates that the desire for high level modeling support need not be irreconcilable with sustained high performance.

Abstraction mechanisms in discrete-event inductive modeling

Abstract: The power of abstraction lies in its ability to deal with "lack" of knowledge. In this regard, success in modeling and simulation rests on discovering useful abstractions that can support objectives of modeling. In our treatment, we refer to "data abstraction" as opposed to "structure simplification" since we consider a system's behavior rather than its structure. A system's behavior can be represented as time varying input/output segments. Given the behavior of a causal, time-invariant system, we define some basic abstraction mechanisms to support inductive modeling. The basis for these abstraction mechanisms are a set of general assumptions which allow consistent abstraction of IO segments. Then, given these assumptions and non-monotonic reasoning paradigm, capable of handling them, we try to tackle the fundamental problem of insufficient knowledge in the realm of inductive modeling. In this way, by making useful abstractions, we can predict a system's unobserved behavior according to a well-defined framework of discrete-event inductive modeling.

High performance simulation-based optimization environment for large scale systems

Abstract: Modelling large scale systems with natural and artificial components requires storage of voluminous amounts of knowledge/information as well as computing speed for simulations to provide reliable answers in reasonable time. Computing technology is becoming powerful enough to support such high performance modelling and simulation. This dissertation proposes a high performance simulation based optimization environment to support the design and modeling of large scale systems with high levels of resolution. The proposed environment consists of three layers--modeling, simulation and searcher layer. The modeling layer employs the Discrete Event System Specification (DEVS) formalism and shows how it provides efficient and effective representation of both continuous and discrete processes in mixed artificial/natural systems necessary to fully exploit available computational resources. Focusing on the portability of DEVS across serial/parallel platforms, the simulation layer adopts object-oriented technology to achieve it. DEVS is implemented in terms of a collection of classes, called containers, using C++. The searcher layer employs Genetic Algorithms to provide generic, robust search capability. In this layer, a class of parallel Genetic Algorithms, called Distributed Asynchronous Genetic Algorithm (DAGA), is developed to provide the speed required for simulation based optimization of large scale systems. This dissertation presents an example of DEVS modeling for a watershed, which is one of the most complex ecosystems. The example shows a well-justified process of abstraction from traditional differential equation models to DEVS representation. An approach is proposed for valid aggregation of spatially distributed systems to reduce the simulation time of watershed models. DEVS representation and spatial aggregation assure relative validity and realism with feasible computational constraints. Throughout the dissertation, several examples of GA optimization are presented to demonstrate the effectiveness of the proposed optimization environment in modeling large scale systems.