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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.


Papers
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Book
07 Mar 1994
TL;DR: The robot, the environment, and the tasks performed by the system were sufficiently paradigmatic to enable initial explorations of many core issues in the development of intelligent autonomous systems.
Abstract: During the late 1960s and early 1970s, an enthusiastic group of researchers at the SRI AI Laboratory focused their energies on a single experimental project in which a mobile robot was being developed that could navigate and push objects around in a multi-room environment (Nilsson [11]) . The project team consisted of many people over the years, including Steve Coles, Richard Duda, Richard Fikes, Tom Garvey, Cordell Green, Peter Hart, John Munson, Nils Nilsson, Bert Raphael, Charlie Rosen, and Earl Sacerdoti. The hardware consisted of a mobile cart, about the size of a small refrigerator, with touch-sensitive "feelers", a television camera, and an optical range-finder. The cart was capable of rolling around an environment consisting of large boxes in rooms separated by walls and doorways; it could push the boxes from one place to another in its world. Its suite of programs consisted of those needed for visual scene analysis (it could recognize boxes, doorways, and room corners), for planning (it could plan sequences of actions to achieve goals), and for converting its plans into intermediatelevel and low-level actions in its world. When the robot moved, its television camera shook so much that it became affectionately known as "Shakey the Robot". The robot, the environment, and the tasks performed by the system were quite simple by today's standards, but they were sufficiently paradigmatic to enable initial explorations of many core issues in the development of intelligent autonomous systems. In particular, they provided the context and motivation for development of the A* search algorithm (Hart et al. [7] ), the STRIPS (Fikes and Nilsson [4] ) and ABSTRIPS (Sacerdoti [ 14] ) planning systems, programs for generalizing and learning macro-operators

33 citations

Journal ArticleDOI

32 citations

Journal ArticleDOI
TL;DR: A set of distinguished scholars and practitioners who were involved in AI's formative stages to describe the most notable trend or controversy during AI's development are asked.
Abstract: The transition to the next millennium gives us an opportunity to reflect on the past and project the future. In this spirit, we have asked a set of distinguished scholars and practitioners who were involved in AI's formative stages to describe the most notable trend or controversy (or nontrend or noncontroversy) during AI's development. The responses provide an interesting characterization of AI-and, in many ways, of the people of AI. We gave our contributors a great deal of flexibility in the nature of their responses. Some provided grand summaries of the history of the field as a whole. Others commented insightfully on more focused topics. Some observed changes and changed along with them. Others are still making advances on research agendas articulated presciently long ago. Some are optimistic. Others are pessimistic. Despite the range, both individually and collectively they provide insights into where we have been and where we are going. Although each contribution is a unique expression of its author's glimpse back through AI's development, there is repetition of important themes that are at the discipline's core. The article serves as an interesting record of where AI is today, as well as setting the stage for what's to come.

31 citations

DOI
30 Dec 1899
TL;DR: In this article, a subjective Bayesian inference method is proposed to deal with the inconsistencies usually found in collections of subjective statements, which is a modification of the traditional formal Bayesian approach.
Abstract: The general problem of drawing inferences from uncertain or incomplete evidence has invited a variety of technical approaches, some mathematically rigorous and some largely informal and intuitive. Most current inference systems in artificial intelligence have emphasized intuitive methods, because the absence of adequate statistical samples forces a reliance on the subjective judgment of human experts. We describe in this paper a subjective Bayesian inference method that realizes some of the advantages of both formal and informal approaches. Of particular interest are the modifications needed to deal with the inconsistencies usually found in collections of subjective statements.

31 citations

Journal ArticleDOI
TL;DR: Modus ponens would generalize when one assigned probabilities (instead of binary truth values) to P and P D Q, and can be bounded as follows.

31 citations


Cited by
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Book
01 Jan 1988
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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.

15,671 citations

Journal ArticleDOI
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

14,635 citations

Book
John R. Koza1
01 Jan 1992
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
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

13,487 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Journal ArticleDOI
TL;DR: This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.

12,962 citations