6433216
XCS Applied To Mapping FPGA Architectures
This paper considers the application of XCS to the complex, real-world problem of mapping Boolean networks to technology-specific layout of field programmable gate arrays (FPGAs). The mapping is formulated as a temporal task, where the XCS's actions are to create blocks (based on an abstract Boolean network) that can be placed in the FPGA, one-at-a-time. Despite the complexity of this task, we demonstrate that the system is effective. We demonstrate the transfer of knowledge stored in an XCS rule set from a small FPGA mapping problem, to a larger problem. We present a novel technique that utilizes a problem-specific finite state machine and two populations of classifiers to treat the problem hierarchically. Final sections of the paper discuss implications and future directions for this work.
33919116
The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques
A system employed by the authors to acquire novel fighter aircraft manoeuvres from combat simulation is more akin to the traditional LCS model than to more recent systems. Given the difficulties often experienced in LCS research on simple problems, one must ask how a relatively primitive LCS has had consistent success in the complex domain of fighter aircraft manoeuvring. This paper presents the fighter aircraft LCS, in greater detail than in previous publications. Positive results from the system are discussed. The paper then focuses on the primary reasons the fighter aircraft LCS has avoided the difficulties of the traditional LCS. The authors believe the system's success has three primary origins: differences in credit assignment, differences in action encoding, and (possibly most importantly) a difference in system goals. In the fighter aircraft system, the goal has been simply the discovery of innovative, novel tactics, rather than online control. The paper concludes by discussing the most salient features of the fighter aircraft learning system, and how those features may be profitably combined with other LCS developments.
209398246
From Kansas to Oz.
31744347
Undergraduate Software Engineering - An Innovative Degree at Swinburne
In this paper we outline the context for the development of an undergraduate degree in Software Engineering in Australia, reflect on curriculum development ideas relevant to such a degree, and describe aspects of the Computer Science degree at Swinburne which, despite its title, may be viewed as a Software Engineering degree. We emphasise the role of project-based units within the degree.
18420869
Simultaneous measurement of steering performance and perceived heading on a curving path
The ability to judge heading (direction of travel) has been the focus of much research, but a role for perceived heading in steering has not been firmly established. Subjects steered down a road consisting of straight and curved segments and made heading judgments along the way. Heading judgments while traversing curved road segments were biased in the direction of the curve by up to 5° and position errors on the same curved roads were highly correlated with heading biases. This correlation was revealed by the simultaneous measurement of steering performance and perceived heading.
6086610
Security for fixed sensor networks
Sensor networks consist of resource-constrained sensors operating in a variety of environments. Given the severe constraints on these sensors, it is a particularly challenging problem to choose and design valid security protocols for such networks. This problem has recently given rise to new research addressing the security issues. This paper presents an overview of the important works, specifically the new mechanisms and protocols, which have been introduced or are still under development in this area.
15635960
Improved variable and value ranking techniques for mining categorical traffic accident data
The ever increasing size of datasets used for data mining and machine learning applications has placed a renewed emphasis on algorithm performance and processing strategies. This paper addresses algorithms for ranking variables in a dataset, as well as for ranking values of a specific variable. We propose two new techniques, called Max Gain (MG) and Sum Max Gain Ratio (SMGR), which are well-correlated with existing techniques, yet are much more intuitive. MG and SMGR were developed for the public safety domain using categorical traffic accident data. Unlike the typical abstract statistical techniques for ranking variables and values, the proposed techniques can be motivated as useful intuitive metrics for non-statistician practitioners in a particular domain. Additionally, the proposed techniques are generally more efficient than the more traditional statistical approaches.