A new algorithm Minimize Vexation, version 10 (MV10) for managing the elevators of a group is presented. Cars are dispatched in a way that maximizes efficiency for all the individuals who use an elevator in the group of elevators. Much information about the individuals in the group is inferred or estimated, greatly aiding the decision making process. A variety of questions regarding novel components of the algorithm are posed, and the answers are provided by way of deep statistical analysis. Insights are offered into the nature of various objective functions and their effects on system performance. The service time of the MV10 algorithm compares favorably to that of known approaches to the problem, demonstrated again through statistical analysis. For comparison purposes and perspective, the elevator dispatching problem is recast in a separate system in which all individual travel plans are known ahead of time, giving an indication of the best attainable efficiency.

We explore incremental assimilation of new knowledge by sequential learning. Of particular interest is how a network of many knowledge layers can be constructed in an on-line manner, such that the learned units represent building blocks of knowledge that serve to compress the overall representation and facilitate transfer. We motivate the need for many layers of knowledge, and we advocate sequential learning as an avenue for promoting the construction of layered knowledge structures. Finally, our novel STL algorithm demonstrates a method for simultaneously acquiring and organizing a collection of concepts and functions as a network from a stream of unstructured information.

For typical classification tasks, all training data are prepared in advance and are supplied to the classifier all at once. This is unnecessarily expensive and incurs overfitting problems, since the individual contributions of the training instances to the classifier are not known. We address this by proposing an interactive incremental framework for image classifier construction, where small numbers of training examples are supplied at each user interaction. After incorporating new training instances, the classifier immediately reclassifies the image to provide the user with instant feedback. This allows the user to choose additional informative training pixels from among the currently misclassified ones. Using a realistic terrain classification task, we demonstrate the potential of our method to generate small and accurate decision tree classifiers from surprisingly few training examples while avoiding overspecialization. We also briefly discuss the novel concept of hierarchical classification, where higher-level classifiers take as input the output of lower-level classifiers. We present preliminary results indicating that within our interactive framework, this is a practical approach to exploiting spatial relationships for classification.

Recent work in feature-based classification has focused on nonparametric techniques that can classify instances even when the underlying feature distributions are unknown. The inference algorithms for training these techniques, however, are designed to maximize the accuracy of the classifier, with all errors weighted equally. In many applications, certain errors are far more costly than others, and the need arises for nonparametric classification techniques that can be trained to optimize task-specific cost functions. This correspondence reviews the linear machine decision tree (LMDT) algorithm for inducing multivariate decision trees, and shows how LMDT can be altered to induce decision trees that minimize arbitrary misclassification cost functions (MCF's). Demonstrations of pixel classification in outdoor scenes show how MCF's can optimize the performance of embedded classifiers within the context of larger image understanding systems.>

This paper presents an approach to learning to control a dynamic physical system. The approach has been implemented in a program named CART, and applied to a simple physical system studied previously by several researchers. Experiments illustrate that a control method is learned in about 16 trials, an improvement over previous learning programs.