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The Department of Defense has always had an interest in simulation systems for training and analysis. Recently, simulation usage in training has been stressed. Simulation systems provide an easy and effective way for trainees to learn military doctrine and small unit tactics. In order to be effective, a simulation must accurately represent the real world. Sensory perception is one important real world influence that should be modeled. Modular Semi-Automated Forces (ModSAF) is a highly advanced computer simulation system that can model the movement, formations, equipment and other aspects of armed forces with precise calculations. In spite of all of the advances in ModSAF and other systems like it, the behaviors of sound and how they affect the warfighter have been scarcely modeled. The goal of this project is to investigate how sound is produced, propagated, and detected (acoustics), the behavioral effects sound has on the human psyche, and model the findings. It includes the development of a computer model of the physical phenomenon relating to sound, to be incorporated within ModSAF or another similar constructive simulation system.

This thesis describes a method for improving Computer Generated Forces (CGF) simulations by extending the Context-Based Reasoning (CxBR) element of Situation Interpretation Metrics (SIMs) as a way to improve the context transition process and overall efficiency. To effectively cope with a large number of possible choices while still exhibiting realistic tactical decision making, the CxBR framework requires extension of SIMs to allow representation of a more diverse set of situations. This approach allows for more effective duplication of human tactical decision making while also preserving the benefits of the competing context concept. By extending the SIMs and more effectively interpreting them) the occurrence of ambiguous context transitions would be significantly less than it currently is) making it possible to significantly reduce the computational needs of the simulation while at the same time improving overall realism. The approach to this problem is described and test results are presented.

Several authors assert that any significant improvement in the efficiency in the development and utilization of simulation models requires the assistance of a Model Management System to automate much of the process. This work develops one aspect of such system: tools fundamental to the specification and analysis of discrete event models. A model specification language is developed which allows analysis of a model specification as the specification is being developed. Model analyses are discussed which (1) detect several types of errors in a model specification, (2) automate the generation of many types of documentation useful during model development and the generation of model documentation useful during the model life cycle, (3) improve model implementations by assisting in the choice of a world view and implementation technique. A model specification in this language is called a condition specification (CS). Formal procedures are developed to transform a cs into a model specification in any of the three traditional worldviews of event scheduling, activity scanning, and process interaction. These transformation procedures are supported by a careful definition of the equivalency of model specifications and by a characterization of each of the three world views. Results are proved which show that the approach of each worldview transformation is valid (i.e., result in equivalent model specifications). This is necessary since the transformation procedures may delete parts of a CS. Both the characterization of each world view and the procedures that transform a cs into each world view provide a better understanding of the nature of each worldview than has existed to date. Several necessary properties for error-free model specifications are identified and defined. While useful test procedures for these properties can be developed, most of these properties are shown to be unverifiable in an absolute sense. That is, it is proved that no algorithm is possible which can detect every instance of several important model specification errors. / Ph. D.