Reactive Control of Distributed Interactive Simulations

Abstract

Rhodes Hall, Ithaca, NY 14853 (607) 255-8005 REPORT DOCUMENTATION PAGEForm Approved OMB NO. Public Reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comment regarding this burden estimates or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and In this paper we propose a distributed control architecture for Distributed Interactive Simulation (DIS) applications which allows for the dynamic integration and communication of interacting processes using inteligent control agents cooperating via a logic communication network. The type of DIS applications which we discuss include the interactive simulation initiative of the US Army to create a realistic warfare training environment by using high speed communications to permit real-time interaction between geographically dispersed warfare simulations with actual combat units in the field, cooperative game playing and virtual manufacturing. In this paper we propose a distributed control architecture for Distributed Interactive Simulation (DIS) applications which allows for the dynamic integration and communication of interacting processes using intelligent control agents cooperating via a logic communication network. The type of DIS applications which we discuss include the interactive simulation initiative of the US Army to create a realistic warfare training environment by using high speed communications to permit real-time interaction between geographically dispersed warfare simulations with actual combat units in the field, cooperative game playing, and virtual manufacturing. Our architecture, termed Multiple Agent Hybrid Control Architecture (MAHCA), is based on recent advancements in hybrid systems theory and applications. Our approach provides for the flexible interoperability of distributed, real-time information systems by generating, in real time, control programs which comply with logical and evolutionary specifications. 185 IntroductionA network of distributed interactive simulations is a hybrid system, that is, a system described by an amalgamation of logical and evolution models. The type of applications that motivated our work here is a distributed interactive simulation initiative of the US Army to create a realistic warfare training environment by using high speed communications to permit real-time interaction between geographically dispersed warfare simulations, with a wide range of capabilities and scale of operation, with actual combat units in the field, see [68,69,70]. Other applications include cooperative game playing over the Internet, virtual manufacturing and virtual enterprises. We will refer to all these...

Repair-Control of Enterprise Systems Using RFID Sensory Data

“…• Reference model: simulates the dynamic representation of the enterprise process under control (see Kohn et al [1994], Lee at al [1994], Nerode et al [1992], Nerode et al [1993]). • Control Generator: computes the optimal control law as a causal map from the performance space ( the product of the sensor space , the parameter space the model frame space and the state space) to the space of actions.…”

Repair-control of enterprise systems using RFID sensory data

“…It consists of six elements: control generator, state estimator, reference model, adapter, learning engine, and command translator. The reference model simulates the dynamic representation of the enterprise process under control Kohn (1992, 1993), Kohn et al (1994), and Lee et al (1994)). The control generator computes the optimal control law as a causal map from the performance space (the Cartesian product of sensor, parameter, model frame, and state spaces) to the space of actions.…”