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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
01 Jan 1971
TL;DR: This paper will concern you to try reading problem solving methods in artificial intelligence as one of the reading material to finish quickly.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading problem solving methods in artificial intelligence as one of the reading material to finish quickly.

1,431 citations

Journal ArticleDOI
01 Feb 1986
TL;DR: In this paper, a semantical generalization of logic in which the truth values of sentences are probabilistic values (between 0 and 1) is presented, which applies to any logical system for which the consistency of a finite set of sentences can be established.
Abstract: Because many artificial intelligence applications require the ability to reason with uncertain knowledge , it is important to seek appropriate generalizations of logic for that case. We present here a semantical generalization of logic in which the truth values of sentences are probabili~ values (between 0 and 1). Our generalization applies to any logical system for which the consistency of a finite set of sentences can be established. The method described in the present paper combines logic with probability theory in such a way that probabilistic logical entaihnent reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1.

1,240 citations

Journal ArticleDOI
TL;DR: Some major new additions to the STRIPS robot problem-solving system are described, including a process for generalizing a plan produced by STriPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.

1,115 citations

Book
15 Aug 1997
TL;DR: Intelligent agents are employed as the central characters in this new introductory text and Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI.
Abstract: Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI. * An evolutionary approach provides a unifying theme * Thorough coverage of important AI ideas, old and new * Frequent use of examples and illustrative diagrams * Extensive coverage of machine learning methods throughout the text * Citations to over 500 references * Comprehensive index Table of Contents 1 Introduction 2 Stimulus-Response Agents 3 Neural Networks 4 Machine Evolution 5 State Machines 6 Robot Vision 7 Agents that Plan 8 Uninformed Search 9 Heuristic Search 10 Planning, Acting, and Learning 11 Alternative Search Formulations and Applications 12 Adversarial Search 13 The Propositional Calculus 14 Resolution in The Propositional Calculus 15 The Predicate Calculus 16 Resolution in the Predicate Calculus 17 Knowledge-Based Systems 18 Representing Commonsense Knowledge 19 Reasoning with Uncertain Information 20 Learning and Acting with Bayes Nets 21 The Situation Calculus 22 Planning 23 Multiple Agents 24 Communication Among Agents 25 Agent Architectures

1,090 citations

01 Apr 1984
TL;DR: The Shakey project led to numerous advances in AI techniques, many of which were reported in the literature, much specific in formation that might be useful in current robotics research appears only in a series of relatively inaccessible SRI technical reports as discussed by the authors.
Abstract: : From 1960 through 1972, the Artificial Intelligence Center at SRI conducted research on a mobile robot system nicknamed "Shakey." Endowed with a limited ability to perceive and model its environment, Shakey could perform tasks that required planning, route finding, and the rearranging of simple objects. Although the Shakey project led to numerous advances in AI techniques, many of which were reported in the literature, much specific in formation that might be useful in current robotics research appears only in a series of relatively inaccessible SRI technical reports. Our purpose here, consequently, is to make this material more readily available by extracting and reprinting those sections of the reports that seem particularly interesting, relevant and important.

761 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