Showing papers by "Bernard P. Zeigler published in 2002"

DEVS-DOC: a modeling and simulation environment enabling distributed codesign

Abstract: An approach to modeling and simulating distributed object computing systems as a set of software components mapped onto a set of networked processing nodes is presented. The modeling approach has clearly separated hardware and software components enabling systems level, distributed codesign engineering. The distributed codesign engineering refers to a system-theoretic approach to concurrent hardware and software systems engineering that provides a tractable method for analyzing the inherent complexities that arise in distributed computing systems. A software abstraction forms a distributed cooperative object (DCO) model to represent interacting software objects. A hardware abstraction forms a loosely coupled network (LCN) model of processing nodes, network gates and interconnecting communication links. The distribution of DCO software across LCN processors forms an object system mapping (OSM). This OSM provides a sufficient specification to allow simulation investigations. In simulation, the behavioral dynamics of the interacting DCO software components load and compete for LCN processing and networking resources. The LCN resource constraints thus impose performance constraints on the interactions of the DCO software objects. Class models of the DCO, LCN, and OSM component structures and behavior dynamics were formally characterized using the discrete event system specification (DEVs) formalism. These class model specifications were implemented in DEVSJAVA, a Java implementation of DEVS. Class models of experimental frame components were developed and implemented to facilitate analysis of the interdependent distributed system behaviors during simulations. Our DEVS-DOC M&S environment enables distributed systems architects, integration engineers and system designers to analyze performance and examine engineering trades of system structures, topologies and technologies. A case study demonstrates the ability to model and simulate a real world system and the complex interactions that arise in distributed computing systems.

The brain-machine disanalogy revisited.

Abstract: Michael Conrad was a pioneer in investigating biological information processing. He believed that there are fundamental lessons to be learned from the structure and behavior of biological brains that we are far from understanding or have implemented in our computers. Accumulation of advances in several fields have confirmed his views in broad outline but not necessarily in some of the strong forms he had tried to establish. For example, his assertion that programmable computers are intrinsically incapable of the brain's efficient and adaptive behavior has not received much examination. Yet, this is clearly a direction that could afford much insight into fundamental differences between brain and machine. In this paper, we pay tribute to Michael, by examining his pioneering thoughts on the brain-machine disanalogy in some depth and from the hindsight of a decade later. We argue that as long as we stay within the frame of reference of classical computation, it is not possible to confirm that programmability places a fundamental limitation on computing power, although the resources required to implement a programmable interface leave fewer resources for actual problem-solving work. However, if we abandon the classical computational frame and adopt one in which the user interacts with the system (artificial or natural) in real time, it becomes easier to examine the key attributes that Michael believed place biological brains on a higher plane of capability than artificial ones. While we then see some of these positive distinctions confirmed (e.g. the limitations of symbol manipulation systems in addressing real-world perception problems), we also see attributes in which the implementation in bioware constrains the behavior of real brains. We conclude by discussing how new insights are emerging, that look at the time-bound problem-solving constraints under which organisms have had to survive and how their so-called 'fast and frugal' faculties are tuned to the environments that coevolved with them. These directions open new paths for a multifaceted understanding of what biological brains do and what we can learn from them. We close by suggesting how the discrete event modeling and simulation paradigm offers a suitable medium for exploring these paths.

An Integrated Modeling and Simulation Methodology for Intelligent Systems Design and Testing

Abstract: : Model continuity refers to the ability to use the same model of a system throughout its design phases. For intelligent systems, we can restrict such continuity to the intelligent control components, and more specifically, the models that implement the system's decision making. behavior. In this paper, we show how a modeling and simulation environment, based on the DEVS formalism, can support model continuity in the design of intelligent systems. For robotic systems, such continuity allows design and testing of the same control logic model through the phases including logical simulation, real-time simulation and actual execution.