Nils J. Nilsson

Bio: Nils J. Nilsson is an academic researcher from Stanford University. The author has contributed to research in topics: Inference & First-order logic. The author has an hindex of 37, co-authored 90 publications receiving 28751 citations. Previous affiliations of Nils J. Nilsson include SRI International & Artificial Intelligence Center.

Sentience in robots: applications and challenges

Abstract: The article provides views from researchers and practitioners directly involved with robotics applications to find out what they think about the applicability of this new generation of more adaptable and user-friendly robots. Aspects discussed include: personal robots in human societies; robot teams in urban search and rescue; space robots and natural language communication; representation and reasoning; and rationality and emotions.

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

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Genetic Programming: On the Programming of Computers by Means of Natural Selection

Abstract: Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).