Improving the performance of cascade correlation neural networks on multimodal functions

Intrinsic qualities of the cascade correlation algorithm make it a popular choice for many researchers wishing to utilize neural networks. Problems arise when the outputs required are highly multimodal over the input domain. The mean squared error of the approximation increases significantly as the number of modes increases. By applying ensembling and early stopping, we show that this error can be reduced by a factor of three. We also present a new technique based on subdivision that we call patchworking. When used in combination with early stopping and ensembling the mean improvement in error is over 10 in some cases

Verification issues for rule-based expert systems

Verification and validation of expert systems is very important for the future success of this technology. Software will never be used in non-trivial applications unless the program developers can assure both users and managers that the software is reliable and generally free from error. Therefore, verification and validation of expert systems must be done. The primary hindrance to effective verification and validation is the use of methodologies which do not produce testable requirements. An extension of the flight technique panels used in previous NASA programs should provide both documented requirements and very high levels of verification for expert systems

Approaches to the verification of rule-based expert systems

Expert systems are a highly useful spinoff of artificial intelligence research. One major stumbling block to extended use of expert systems is the lack of well-defined verification and validation (V and V) methodologies. Since expert systems are computer programs, the definitions of verification and validation from conventional software are applicable. The primary difficulty with expert systems is the use of development methodologies which do not support effective V and V. If proper techniques are used to document requirements, V and V of rule-based expert systems is possible, and may be easier than with conventional code. For NASA applications, the flight technique panels used in previous programs should provide an excellent way to verify the rules used in expert systems. There are, however, some inherent differences in expert systems that will affect V and V considerations

An expert system development methodology which supports verification and validation

Expert systems have demonstrated commercial viability in a wide range of applications, but still face some obstacles to widespread use. A major stumbling block is the lack of well defined verification and validation (V and V) techniques. The primary difficulty with expert system V and V is the use of development methodologies which do not support V and V. As with conventional code, the key to effective V and V is the development methodology. An expert system development methodology is described which is based upon a panel review approach, that allows input from all parties concerned with the expert system

A study of early stopping, ensembling, and patchworking for cascade correlation neural networks

The constructive topology of the cascade correlation algorithm makes it a popular choice for many researchers wishing to utilize neural networks. However, for multimodal problems, the mean squared error of the approximation increases significantly as the number of modes increases. The components of this error will comprise both bias and variance and we provide formulae for estimating these values from mean squared errors alone. We achieve a near threefold reduction in the overall error by using early stopping and ensembling. Also described is a new subdivision technique that we call patchworking. Patchworking, when used in combination with early stopping and ensembling, can achieve an order of magnitude improvement in the error. Also presented is an approach for validating the quality of a neural network’s training, without the explicit use of a testing dataset

Reach modelling for drive-up self-service

People using a self-service terminal such as an automated teller machine (ATM) tend to adjust their physical position throughout a transaction. This is particularly apparent with terminals that are designed to be used from a vehicle (i.e. drive up automated teller machines or ATMs). Existing predictive tools tend to focus on static reach and provide limited predictions for how far people are willing to stretch to complete a task. Drive-up self-service products have 3 main challenges: the variability of vehicles, people and driver behaviour. Such conventional tools are therefore of limited use in understanding how much people are willing to move to use a self-service terminal. Work is described to build in-house predictive models based on 2 large empirical studies of reach in a drive up installation. These 2 studies assessed comfortable and extended reach from 10 vehicle categories. Extended reach was defined as stretching/leaning as far as participants would normally be willing to in order to complete a drive-up transaction. Findings from these studies indicated that participants are prepared to adopt more extreme postures at drive-up than in other situations with extended reach at drive-up being significantly different to what might be seen at a walk-up kiosk

CLIPS: An expert system tool for delivery and training

The C Language Integrated Production System (CLIPS) is a forward chaining rule-based language. The requirements necessary for an expert system tool which is used for development, delivery, and training are examined. Because of its high portability, low cost, and ease of integration with external systems, CLIPS has great potential as an expert system tool for delivery and training. In addition, its representation flexibility, debugging aids, and performance, along with its other strengths, make it a viable alternative for expert system development

The Problem of Good Intentions: Challenges Arising from State Mandated University-Wide Sexual Misconduct Reporting

Legislatures and regulators struggle to create effective legal mechanisms to address the misreporting and underreporting of sexual misconduct on college campuses. The problems are clear: how does the law balance the desire to fully support victims of sexual misconduct by providing access to supportive measures and complaint resolution options, while also honoring the desire of some victims not to have private information shared with others? While some employees have failed to report known instances of sexual misconduct based on inappropriate grounds, others do so based on a desire to respect the victim’s wishes. How should these problems, which may stem from organizational cultures, be solved through legislation or regulation? Federal laws--Title IX and the Clery Act--impose reporting duties on only some employees, based on their particular role, but beginning in 2019, the Texas Legislature went a step further and mandated university-wide sexual misconduct reporting for all employees. The penalties for failure to report are severe: termination and prosecution. While well-intentioned, this new Texas law nevertheless creates many problems that undermine its effectiveness. We address Texas Senate Bill 212 in its larger national context, offer several general critiques, highlight the special problems associated with the application of the law at faith-based universities, and make suggestions for university administrators and future legislative action in an attempt to refine the scope of the law to better address the underreporting problem