Showing papers by "Nils J. Nilsson published in 1969"

A mobius automation: an application of artificial intelligence techniques

Abstract: A research project applying artificial intelligence techniques to the development of integrated robot systems Is described. The experimental facility consists of an SDS-940 computer and associated programs controlling a wheeled vehicle that carries a TV camera and other sensors. The primary emphasis is on the development of a system of programs for processing sensory data from the vehicle, for storing relevant information about the environment, and for planning the sequence of motor actions necessary to accomplish tasks in the environment. A typical task performed by our present system requires the robot vehicle to rearrange (by pushing) simple objects in its environment A novel feature of our approach is the use of a formal theorem-proving system to plan the execution of high-level functions as a sequence of other, perhaps lower-level, functions. The execution of these, in turn, requires additional planning at lower levels. The main theme of the research is the integration of the necessary planning systems, models of the world, and sensory processing systems into an efficient whole capable of performing a wide range of tasks in a real environment.

Survey of pattern recognition

Abstract: One of the important uses of computers in clinical medicine is for the classification or screening of data. Examples abound where high speed and inexpensive but reliable automatic classification is desired. Electrocardiographs must be classified as healthy or abnormal. Differential white blood cell counts require the ability to discriminate between the various types of cells. Cancerdetecting smears must be sorted as normal or abnormal. It has become commonplace to speak of these kinds of sorting tasks as pattern-recognition problems and to advocate the application of pattern-recognition techniques for their solution. A wide range of such techniques exists. Some of them have been used by statisticians for years; others have been developed only recently as a result of the availability of high-speed computers. In this paper I shall describe some of the more common pattern-recognition methods. Although I shall cite some clinical applications of pattern recognition as illustrative examples, it is not my purpose to report on these in detail. An extensive literature'" already exists that provides numerous examples of the successful use of these methods. Indeed, many of the papers in this monograph will report the results of automatic classification experiments. I only hope that I can explain in this paper some of the unifying ideas that underlie many of the pattern-recognition methods already being applied. (See also a very good introductory article by Rosen.*) In order to apply pattern-recognition techniques, the phenomenon to be classified must be represented in some "computer-acceptable" form. Furthermore, the representation method used depends critically on the type of phenomenon. Thus, for photomicrographs of chromosomes, we might use complex picture-processing methods to represent the picture as a list of numbers, whereas, for a medical history record, it may only be possible to represent the data on the form as a list of nonnumerical symbols. The phenomenon to be classified is called the event and its representation is called the data in order to distinguish between them. In this paper, I shall be primarily concerned with those pattern-recognition techniques for sorting numerical data, that is, representations that are in the form of a list of numbers. First, I shall describe several data-classifying methods (assuming that the event