Terrain Representation And Reasoning In Computer Generated Forces : A Survey Of Computer Generated Forces Systems And How They Represent And Reason About Terrain

Report on a survey of computer systems used to produce realistic or intelligent behavior by autonomous entities in simulation systems. In particular, it is concerned with the data structures used by computer generated forces systems to represent terrain and the algorithmic approaches used by those systems to reason about terrain

Integrated Eagle/BDS-D Research Resolution Of Behavior Representation Methods Of CCTT SAF And ModSAF Interim Report: Behavior Representation Integration Techniques

Report on the first engineering change proposal, including track Eagle units within computer generated forces testbed, base Eagle unit detection on line of sight, platoon and batallion disaggregation, and manned simulators

Software Frameworks for Model Composition

A software framework is an architecture or infrastructure intended to enable the integration and interoperation of software components. Specialized types of software frameworks are those specifically intended to support the composition of models or other components within a simulation system. Such frameworks are intended to simplify the process of assembling a complex model or simulation system from simpler component models as well as to promote the reuse of the component models. Several different types of software frameworks for model composition have been designed and implemented; those types include common library, product line architecture, interoperability protocol, object model, formal, and integrative environment. The various framework types have different components, processes for composing models, and intended applications. In this survey the fundamental terms and concepts of software frameworks for model composition are presented, the different types of such frameworks are explained and compared, and important examples of each type are described

Computer-Generated Forces In Distributed Interactive Simulation

Distributed Interactive Simulation (DIS) is an architecture for building large-scale simulation models from a set of independent simulator nodes communicating via a common network protocol. DIS is most often used to create a simulated battlefield for military training. Computer Generated Forces (CGF) systems control large numbers of autonomous battlefield entities in a DIS simulation using computer equipment and software rather than humans in simulators. CGF entities serve as both enemy forces and supplemental friendly forces in a DIS exercise. Research into various aspects of CGF systems is ongoing. Several CGF systems have been implemented

Electronic Warfare And Distributed Interactive Simulation

The experimentation and demonstration of electronic warfare (EW) capabilities in distributed interactive simulation (DIS) was performed through the development of these capabilities in the Institute for Simulation and Training\u27s (1ST) Computer Generated Forces (CGF) Testbed. The 1ST CGF Testbed with EW capabilities can create land, sea, and air entities that can generate and receive electromagnetic emissions across a DIS network. Previous efforts in behavior definition for CGF systems, including the 1ST CGF Testbed, have emphasized land forces performing visual contact engagements. Adding EW capabilities to the CGF Testbed has expanded the sensing horizon of entities beyond visual range providing long range contacts for air and sea engagements. This work provided useful testing of the DIS Standards in the area of electronic warfare

Heuristic methods for synthesizing realistic social networks based on personality compatibility

Abstract Social structures and interpersonal relationships may be represented as social networks consisting of nodes corresponding to people and links between pairs of nodes corresponding to relationships between those people. Social networks can be constructed by examining actual groups of people and identifying the relationships of interest between them. However, there are circumstances where such empirical social networks are unavailable or their use would be undesirable. Consequently, methods to generate synthetic social networks that are not identical to real-world networks but have desired structural similarities to them have been developed. A process for generating synthetic social networks based on assigning human personality types to the nodes and then adding links between nodes based on the compatibility of the nodes’ personalities was developed. Two new algorithms, Probability Search and Compatibility-Degree Matching, for finding an effective assignment of personality types to the nodes were developed, implemented, and tested. The two algorithms were evaluated in terms of realism, i.e., the similarity of the generated synthetic social to exemplar real-world social networks, for 14 different real-world social networks using 20 standard quantitative network metrics. Both search algorithms produced networks that were, on average, more realistic than a standard network generation algorithm that does not use personality, the Configuration Model. The algorithms were also evaluated in terms of computational complexity