Author

# Nils J. Nilsson

Other affiliations: SRI International, Artificial Intelligence Center

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

##### Papers published on a yearly basis

##### Papers

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TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.

Abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.

8,780 citations

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01 Jan 1980TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.

Abstract: A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, "Principles of Artificial Intelligence" describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. "Principles of Artificial Intelligence"evolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

3,688 citations

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TL;DR: In this paper, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model in which a given goal formula can be proven to be true.

Abstract: We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model in which a given goal formula can be proven to be true. STRIPS represents a world model as an arbitrary collection in first-order predicate calculus formulas and is designed to work with models consisting of large numbers of formula. It employs a resolution theorem prover to answer questions of particular models and uses means-ends analysis to guide it to the desired goal-satisfying model.

2,658 citations

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31 Oct 1995

TL;DR: In this article, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true.

Abstract: We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true. STRIPS represents a world n,~del as an arbitrary collection of first-order predicate calculus formulas and is designed to work with .models consisting of large numbers of formulas. It employs a resolution theorem prover to answer questions of particular models and uses means-ends analysis to guide it to the desired goal-satisfying model.

1,792 citations

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15 Jul 1987TL;DR: Typographical Conventions 1 Introduction 1 Bibliographical and Historical Remarks Exercises 2 Declarative Knowledge 2.1 Conceptualization 2.2 Predicate Calculus 2.3 Semantics 2.4 Blocks World Example 2.5 Circuits 2.6 Algebraic Examples 2.7 List Examples 1.9 Specialized Languages 2.8 Reasoning with Uncertain Reasoning 3.1 Probabilities of Sentences 3.4 Provability 3.5 Proving Provability

Abstract: Typographical Conventions 1 Introduction 1.1 Bibliographical and Historical Remarks Exercises 2 Declarative Knowledge 2.1 Conceptualization 2.2 Predicate Calculus 2.3 Semantics 2.4 Blocks World Example 2.5 Circuits Example 2.6 Algebraic Examples 2.7 List Examples 2.8 Natural-Language Examples 2.9 Specialized Languages 2.10 Bibliographical and Historical Remarks Exercises 3 Inference 3.1 Derivability 3.2 Inference Procedures 3.3 Logical Implication 3.4 Provability 3.5 Proving Provability 3.6 Bibliographical and Historical Remarks Exercises 4 Resolution 4.1 Clausal Form 4.2 Unification 4.3 Resolution Principle 4.4 Resolution 4.5 Unsatisfiability 4.6 True-or-False Questions 4.7 Fill-in-the-Blank Questions 4.8 Circuits Example 4.9 Mathematics Example 4.10 Soundness and Completeness 4.11 Resolution and Equality 4.12 Bibliographical and Historical Remarks Exercises 5 Resolution Strategies 5.1 Deletion Strategies 5.2 Unit Resolution 5.3 Input Resolution 5.4 Linear Resolution 5.5 Set of Support Resolution 5.6 Ordered Resolution 5.7 Directed Resolution 5.8 Sequential Constraint Satisfaction 5.9 Bibliographical and Historical Remarks Exercises 6 Nonmonotonic Reasoning 6.1 The Closed-World Assumption 6.2 Predicate Completion 6.3 Taxonomic Hierarchies and Default Reasoning 6.4 Circumscription 6.5 More General Forms of Circumscription 6.6 Default Theories 6.7 Bibliographical and Historical Remarks Exercises 7 Induction 7.1 Induction 7.2 Concept Formation 7.3 Experiment Generation 7.4 Bibliographical and Historical Remarks Exercises 8 Reasoning with Uncertain Beliefs 8.1 Probabilities of Sentences 8.2 Using Bayes' Rule in Uncertain Reasoning 8.3 Uncertain Reasoning in Expert Systems 8.4 Probabilistic Logic 8.5 Probabilistic Entailment 8.6 Computations Appropriate for Small Matrices 8.7 Dealing with Large Matrices 8.8 Probabilities Conditioned on Specific Information 8.9 Bibliographical and Historical Remarks Exercises 9 Knowledge and Belief 9.1 Preliminaries 9.2 Sentential Logics of Belief 9.3 Proof Methods 9.4 Nested Beliefs 9.5 Quantifying-In 9.6 Proof Methods for Quantified Beliefs 9.7 Knowing What Something Is 9.8 Possible-Worlds Logics 9.9 Properties of Knowledge 9.10 Properties of Belief 9.11 Group Knowledge 9.12 Equality, Quantification, and Knowledge 9.13 Bibliographical and Historical Remarks Exercises 10 Metaknowledge and Metareasoning 10.1 Metalanguage 10.2 Clausal Form 10.3 Resolution Principle 10.4 Inference Procedures 10.5 Derivability and Belief 10.6 Metalevel Reasoning 10.7 Bilevel Reasoning 10.8 Reflection 10.9 Bibliographical and Historical Remarks Exercises 11 State and Change 11.1 States 11.2 Actions 11.3 The Frame Problem 11.4 Action Ordering 11.5 Conditionality 11.6 Bibliographical and Historical Remarks Exercises 12 Planning 12.1 Initial State 12.2 Goals 12.3 Actions 12.4 Plans 12.5 Green's Method 12.6 Action Blocks 12.7 Conditional Plans 12.8 Planning Direction 12.9 Unachievability Pruning 12.10 State Alignment 12.11 Frame-Axiom Suppression 12.12 Goal Regression 12.13 State Differences 12.14 Bibliographical and Historical Remarks Exercises 13 Intelligent-Agent Architecture 13.1 Tropistic Agents 13.2 Hysteretic Agents 13.3 Knowledge-Level Agents 13.4 Stepped Knowledge-Level Agents 13.5 Fidelity 13.6 Deliberate Agents 13.7 Bibliographical and Historical Remarks Exercises Answers to Exercises A.1 Introduction A.2 Declarative Knowledge A.3 Inference A.4 Resolution A.5 Resolution Strategies A.6 Nonmonotonic Reasoning A.7 Induction A.8 Reasoning with Uncertain Beliefs A.9 Knowledge and Belief A.10 Metaknowledge and Metareasoning A.11 State and Change A.12 Planning A.13 Intelligent-Agent Architecture References Index

1,516 citations

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01 Jan 1988TL;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 uncertaintyand 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 recognitionin 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.

15,149 citations

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01 Jan 1992TL;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

13,137 citations

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24 Aug 1981TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.

Abstract: Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.

12,938 citations

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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.

Abstract: To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.

12,418 citations

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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.).

12,323 citations