Many decision tools and complex decision systems require components that use learning technology to improve the quality of the decisions, based on observations (such as sensor data). In order to employ these tools and systems in high- or medium-risk applications, the design, implementation, and deployment process needs to follow principled verification, validation, and testing procedures that assure a reliable operation. This task is far from being trivial because of the very nature of learning - a technology that provides tools for making decisions under uncertainty. Only little research efforts have been dedicated so far to validating and testing learning-based systems. This paper describes a novel tool for the testing and the validation of learning systems and a set of statistical tests that are employed by this tool for the assessment of learned classification decisions. We also describe some aspects of the underlying theoretical and experimental framework for the validation and testing of systems that learn.

This correspondence describes a method by which range data from a sonar rangefinder can be used to determine the two-dimensional position and orientation of a mobile robot inside a room. The plan of the room is modeled as a list of segments indicating the positions of walls. The algorithm works by correlating straight segments in the range data against the room model, then eliminating implausible configurations using the sonar barrier test, which exploits physical constraints on sonar data. The approach is extremely tolerant of noise and clutter. Transient objects such as furniture and people need not be included in the room model, and very noisy, low-resolution sensors can be used. The algorithm's performance is demonstrated using a Polaroid Ultrasonic Rangefinder.

We review some of the open issues in computational stereo. In particular, we will discuss the problem of extracting better matching primitives and of dealing with occlusions. Markov Random Field models - an extension of standard regularization - suggest sophisticated stereo matching algorithms. They are, however, ill-suited to efficient, real-time applications. We will conclude reviewing a new simple but fast algorithm implemented by one of us (Drumheller, 1986) on the TMC Connection Machine (TM) computer. Some of its features are: (a) the potential for combining different primitives, including color information; (b) the use of a stronger and new formulation of the uniqueness constraint; and (c) its disparity representation that maps efficiently into the architecture of the Connection Machine computer.