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A TRAINING SIMULATION SYSTEM WITH REALISTIC AUTONOMOUS SHIP CONTROL
Author(s) -
Nicolescu Monica,
Leigh Ryan,
Olenderski Adam,
Louis Sushil,
Dascalu Sergiu,
Miles Chris,
Quiroz Juan,
Aleson Ryan
Publication year - 2007
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2007.00318.x
Subject(s) - computer science , focus (optics) , training (meteorology) , control (management) , human–computer interaction , interface (matter) , mechanism (biology) , artificial intelligence , simulation , parallel computing , meteorology , philosophy , physics , bubble , epistemology , maximum bubble pressure method , optics
In this article we present a computational approach to developing effective training systems for virtual simulation environments. In particular, we focus on a Naval simulation system, used for training of conning officers. The currently existing training solutions require multiple expert personnel to control each vessel in a training scenario, or are cumbersome to use by a single instructor. The inability of current technology to provide an automated mechanism for competitive realistic boat behaviors thus compromises the goal of flexible, anytime, anywhere training. In this article we propose an approach that reduces the time and effort required for training of conning officers, by integrating novel approaches to autonomous control within a simulation environment. Our solution is to develop intelligent, autonomous controllers that drive the behavior of each boat. To increase the system's efficiency we provide a mechanism for creating such controllers, from the demonstration of a navigation expert, using a simple programming interface. In addition, our approach deals with two significant and related challenges: the realism of behavior exhibited by the automated boats and their real‐time response to changes in the environment. In this article, we describe the control architecture we developed that enables the real‐time response of boats and the repertoire of realistic behaviors we designed for this application. We also present our approach for facilitating the automatic authoring of training scenarios and we demonstrate the capabilities of our system with experimental results.