Workshop 1: Virtual Environment Usability Engineering

The Naval Air Warfare Center Training Systems Division developed a new automated approach to virtual environment (VE) usability assessment. The resulting scheme is called Multi-Criteria Assessment of Usability for Virtual Environments (MAUVE). This report presents the latest in VE usability engineering approaches, including the MAUVE evaluation approach

Generalized Entropy For Splitting On Numerical Attributes In Decision Trees

Decision Trees are well known for their training efficiency and their interpretable knowledge representation. They apply a greedy search and a divide-and-conquer approach to learn patterns. The greedy search is based on the evaluation criterion on the candidate splits at each node. Although research has been performed on various such criteria, there is no significant improvement from the classical split approaches introduced in the early decision tree literature. This paper presents a new evaluation rule to determine candidate splits in decision tree classifiers. The experiments show that this new evaluation rule reduces the size of the resulting tree, while maintaining the trees accuracy

Usability Engineering Of Virtual Environments (Ves): Identifying Multiple Criteria That Drive Effective Ve System Design

Designing usable and effective interactive virtual environment (VE) systems is a new challenge for system developers and human factors specialists. In particular, traditional usability principles do not consider characteristics unique to VE systems, such as the design of wayfinding and navigational techniques, object selection and manipulation, as well as integration of visual, auditory and haptic system outputs. VE designers must enhance presence, immersion, and system comfort, while minimizing sickness and deleterious aftereffects. Through the development of a multi-criteria assessment technique, the current effort categorizes and integrates these VE attributes into a systematic approach to designing and evaluating VE usability. Validation exercises suggest this technique, the Multi-criteria Assessment of Usability for Virtual Environments (MAUVE) system, provides a structured approach for achieving usability in VE system design and evaluation. Applications for this research include military, entertainment, and any other interactive system that seeks to provide an enjoyable and effective user experience. © 2003 Elsevier Science Ltd. All rights reserved

Scheduling Setup Changes At Bottleneck Workstations In Semiconductor Manufacturing

This paper presents a scheduling heuristic to aid the operators in semiconductor fabrication facilities (commonly referred to as fabs) in choosing what type of lots to process next and whether to change machine setup in order to reduce cycle time. Specifically, this study focused on developing a scheduling heuristic for ion implanters at Cirent Semiconductor (currently Agere Systems) in Orlando, Florida, where implanters are considered to be a bottleneck workstation. The re-entrant flow of production passes several times through the implanters at different stages of the wafer production, requiring changes to the current settings of the workstations and thus incurring a significant setup time. The scheduling heuristic aims at balancing workload levels for implanters processing jobs at different stages of the wafer production lifecycle. This is accomplished by first processing those jobs that contribute most to the increase in inventory levels at the bottleneck workstation. The measures used to evaluate the performance of the proposed heuristic were mean cycle time, mean work in process (WIP), and standard deviation of cycle time. The performance of the proposed heuristic was compared with the scheduling rules currently in use and other commonly used dispatching rules using a validated simulation model. Simulation results showed that the introduced heuristic performs better than all other rules in terms of mean cycle time and WIP in all cases, and better in terms of standard deviation of cycle time for most cases tested. The heuristic can be used at any bottleneck workstation that processes products at different stages of their production cycle and that requires a significant setup time

Efficient Evolution Of Art Neural Networks

Genetic algorithms have been used to evolve several neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). The resulting architectures were shown to achieve competitive generalization and exceptionally small size. A major concern regarding these architectures, and any evolved neural network architecture in general, is the added overhead in terms of computational time needed to produce the finally evolved network. In this paper we investigate ways of reducing this computational overhead by reducing the computations needed for the calculation of the fitness value of the evolved ART architectures. The results obtained in this paper can be directly extended to many other evolutionary neural network architectures, beyond the studied evolution of ART neural network architectures. © 2008 IEEE

Ag-Art: An Adaptive Approach To Evolving Art Architectures

This paper focuses on classification problems, and in particular on the evolution of ARTMAP architectures using genetic algorithms, with the objective of improving generalization performance and alleviating the adaptive resonance theory (ART) category proliferation problem. In a previous effort, we introduced evolutionary fuzzy ARTMAP (FAM), referred to as genetic Fuzzy ARTMAP (GFAM). In this paper we apply an improved genetic algorithm to FAM and extend these ideas to two other ART architectures; ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). One of the major advantages of the proposed improved genetic algorithm is that it adapts the GA parameters automatically, and in a way that takes into consideration the intricacies of the classification problem under consideration. The resulting genetically engineered ART architectures are justifiably referred to as AG-FAM, AG-EAM and AG-GAM or collectively as AG-ART (adaptive genetically engineered ART). We compare the performance (in terms of accuracy, size, and computational cost) of the AG-ART architectures with GFAM, and other ART architectures that have appeared in the literature and attempted to solve the category proliferation problem. Our results demonstrate that AG-ART architectures exhibit better performance than their other ART counterparts (semi-supervised ART) and better performance than GFAM. We also compare AG-ART\u27s performance to other related results published in the classification literature, and demonstrate that AG-ART architectures exhibit competitive generalization performance and, quite often, produce smaller size classifiers in solving the same classification problems. We also show that AG-ART\u27s performance gains are achieved within a reasonable computational budget. © 2008 Elsevier B.V. All rights reserved

Using Genetic Algorithms And An Indifference-Zone Ranking And Selection Procedure Under Common Random Numbers For Simulation Optimisation

Genetic algorithms (GAs) are one of the many optimisation methodologies that have been used in conjunction with simulation modelling. The most critical step with a GA is the assignment of the selective probabilities to the alternatives. Selective probabilities are assigned based on the alternatives estimated performances which are obtained using simulation. An accurate estimate should be obtained to reduce the number of cases in which the search is oriented towards the wrong direction. Furthermores, it is important to obtain this estimate without many replications. This study proposes a simulation optimisation methodology that combines the GA and an indifference-zone (IZ) ranking and selection procedure under common random numbers (CRN). By using an IZ procedure, a statistical guarantee can be made about the direction in which the search should progress as well as a statistical guarantee about the results from the search. Furthermore, using CRN significantly reduces the required number of replications. © 2012 Operational Research Society Ltd. All rights reserved

Mo-Gart: Multiobjective Genetic Art Architectures

In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultaneously the weights, as well as the topology of three well-known ART architectures; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MO-GEAM, or MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit (accuracy on unseen data (generalization) and size (number of categories created)). MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. © 2008 IEEE