Qualitative models arising in the artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill-understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning. Categories and Subject Descriptors: I.2.4 [Artificial Intelligen...

Over the last decade, a dramatic increase has been observed in the ability of individual experimental scientists to generate and store data, which has not been matched by an equivalent development of adequate data management tools. In this paper, we present the results of our efforts to develop a Desktop Experiment Management Environment that many experimental scientists would like to have on their desk. The environment is called ZOO and is developed in collaboration with domain scientists from Soil Sciences and Biochemistry. We first describe the overall architecture of ZOO, and then focus on key features of its various components. We specifically emphasize aspects of the object-oriented database server that is at the core of the system, the experimentation manager that initiates the execution of experiments as a result of scientists' requests, and the mechanisms that the modules of the system use to communicate between them. Finally, we briefly discuss our experiences with the use ...

This work describes some of the extensions included into a tool used to study, model and simulate cellular models. The environment is based on the Cell-DEVS paradigms. The main extensions are devoted to define generic cell spaces, and are based on the formal definitions for n-dimensional Cell-DEVS models. A cell specification language used to define the model's behavior was redefined to include these extensions. In this way, very complex cell based systems can be built in a simple fashion, allowing reductions in the development, checking and maintenance times of the components.

Abstract not found

Traditional computer simulation terminology includes taxonomic divisions with terms such as "discrete event," "continuous," and "process oriented." Even though such terms have become familiar to simulation researchers, the terminology is distinct from other disciplines ---such as artificial intelligence and software engineering--- which have similar goals relating specifically to modelling dynamic systems. There is a need to unify terminology among these disciplines so that system modelling is formalized in a common framework. We present a perspective that serves to characterize simulation models in terms of their procedural versus declarative orientations since these two orientations are prevalent throughout most modelling disciplines that we have encountered. We used a sample dynamic system (e.g., two jug problem) found in artificial intelligence to highlight the connecting threads in system modelling within each discipline. Moreover, in teaching simulation students using this perspe...

The field of parallel discrete event simulation is entering a period of self-assessment. Fifteen years of investigation has witnessed great strides in techniques for efficiently executing discrete event simulations on parallel and distributed machines. Still, the discrete event simulation community at large has failed to recognize many of these results. The central question is, why has this occurred ? One possible reason is an apparent disagreement in both the focus and objectives of the parallel discrete event simulation research community (primarily computer scientists) and the discrete event simulation community (a widely diverse group including, operations researchers, management scientists, mathematicians, and statisticians, as well as computer scientists). An examination of parallel discrete event simulation from a modeling methodological perspective illustrates some of these differences and reveals potentials for their resolution. 1 INTRODUCTION In a recent series of articles ...

To increase speed and reliability of operation, multiple computers are replacing uniprocessors and wired-logic controllers in modern robots and industrial control systems. However, performance increases are not attained by such hardware alone. The operating software controlling the robots or control systems must exploit the possible parallelism of various control tasks in order to perform the necessary computations within given real-time and reliability constraints. Such software consists of both control programs written by application programmers and operating system software offering means of task scheduling, intertask communication, and device control. The Generalized Executive for real-time Multiprocessor applications (GEM) is an operating system that addresses several requirements of operating software. First, when using GEM, programmers can select one of two different types of tasks differing in size, called processes and microprocesses. Second, the scheduling calls offered by GEM permit the implementation of several models of task interaction. Third, GEM supports multiple models of communication with a parameterized communication mechanism. Fourth, GEM is closely coupled to prototype real-time programming environments that provide programming support for the models of computation offered by the operating system. GEM is being used on a multiprocessor with robotics application software of substantial size and complexity.

Computer simulation is a fundamental discipline for studying complex systems. Like any other discipline, simulation must grow and be fine-tuned so that it maintains its position as the base methodology for doing computational science and constructing digital worlds. We discuss ten areas outside of simulation and demonstrate growth by identifying relationships between simulation and each of the areas. We outline each field by describing it briefly and then specifying outstanding issues which remain to be resolved. We have found that we are better able to characterize basic simulation methodology by integrating and extending simulation within the context of other fields. INTRODUCTION The field of computer simulation is approximately forty years old, and is still vibrant and growing. As technology develops faster hardware, old forms of simulation are made faster, and new varieties of simulation emerge through an extension process. Extending the core simulation knowledge base involves tak...

,s’imulution of landscape ecosystems with high reab-ism demands comput~ing power greatly exceeding that of current workstation technology. However, the prospects are excellent that modelling and simulation environrizents may be implemented on next-generation high, performance, heterogeneous distrib.uted comput-ing platforms. Computing technology is becoming powerful enough to support the,voluminous amounts of ~1l,o,~oledge/illformatioiz necessary for representing such systems and the speed required of simulations to provide reliuble answers in reasonable time. This pa-per provrdes an overview of n project to develop a high performance modelling and simulation environment to.support modelling of large-scale, high resolution land-.scape systems. L High performance simulation ‘The pa.per reports on design a,nd henchma.rking of a high performance computing environment support-iug simulat,ion of landscape ecosystems at high lev-(21s of resolut#ion and encompa.ssing la,rge areas, such a.s forests and watersheds. We report on experi-ence ga,ined in an NSF-ARPA sponsored Grand Chal-lenge Applica.tion Group project whose goals are: 1) constructing a. modelling and simulation environment U-rat employs massively parallel processing and Dis-crete Event, System Specificat,ion(DEVS) formalized models t.0 simulate interact,ions of ecosystem processes at srlect,able scales of spa.ce and time, 2) integrating, a.8 intrinsic to t,he environment, Geographical Infor-mation System(GIS) dat,a bases to provided realistic descript8ions of 3-dimensiona, landscapes, and 3) sup-porting experilllentation and interpretation through

ion Tree) which organizes the multimodels based on the abstraction relationship to facilitate the optimal model selection process, and 3) Selection of the optimal model which guarantees to deliver simulation results by the given amount of time. A more detailed model (low abstraction model) is selected when we have enough time to simulate, while a less detailed model (high abstraction model) is selected when the deadline is immediate. The basic idea of selection is to trade structural information for a faster runtime while minimizing the loss of behavioral information. We propose two possible approaches for the selection: an integer programming based-approach and a searchbased approach. By systematically handling simulation deadlines while minimizing the modeler's interventions, OOPM/RT provides an efficient modeling environment for real-time systems. Categories and Subject Descriptors: I.6.5 [Simulation and Modeling]: Model Development General Terms: Modeling Methodology, Real-Time S...