Showing papers by "Nils J. Nilsson published in 1981"

Readings in Artificial Intelligence

Abstract: Readings in Artificial Intelligence focuses on the principles, methodologies, advancements, and approaches involved in artificial intelligence. The selection first elaborates on representations of problems of reasoning about actions, a problem similarity approach to devising heuristics, and optimal search strategies for speech understanding control. Discussions focus on comparison with existing speech understanding systems, empirical comparisons of the different strategies, analysis of distance function approximation, problem similarity, problems of reasoning about action, search for solution in the reduction system, and relationship between the initial search space and the higher level search space. The book then examines consistency in networks of relations, non-resolution theorem proving, using rewriting rules for connection graphs to prove theorems, and closed world data bases. The manuscript tackles a truth maintenance system, elements of a plan-based theory of speech acts, and reasoning about knowledge and action. Topics include problems in reasoning about knowledge, integration knowledge and action, models of plans, compositional adequacy, truth maintenance mechanisms, dialectical arguments, and assumptions and the problem of control. The selection is a valuable reference for researchers wanting to explore the field of artificial intelligence. Table of Contents Preface Acknowledgments Chapter 1 / Search and Search Representations On Representations of Problems of Reasoning About Actions A Problem Similarity Approach to Devising Heuristics: First Results Optimal Search Strategies for Speech-Understanding Control Consistency in Networks of Relations The B* Tree Search Algorithm: A Best-First Proof Procedure Chapter 2 / Deduction Non-Resolution Theorem Proving Using Rewriting Rules for Connection Graphs to Prove Theorems On Closed World Data Bases A Deductive Approach to Program Synthesis Prolegomena to a Theory of Mechanized Formal Reasoning Subjective Bayesian Methods for Rule-Based Inference Systems Chapter 3 / Problem-Solving and Planning Application of Theorem Proving to Problem Solving The Frame Problem and Related Problems in Artificial Intelligence Learning and Executing Generalized Robot Plans Achieving Several Goals Simultaneously Planning and Meta-Planning Chapter 4 / Expert Systems and AI Applications An Experiment in Knowledge-Based Automatic Programming Dendral and Meta-Dendral: Their Applications Dimension Consultation Systems for Physicians Model Design in the PROSPECTOR Consultant System for Mineral Exploration The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty Using Patterns and Plans in Chess Interactive Transfer of Expertise: Acquisition of New Inference Rules Chapter 5 / Advanced Topics Some Philosophical Problems from the Standpoint of Artificial Intelligence The Logic of Frames Epistemological Problems of Artificial Intelligence Circumscription - A Form of Non-Monotonic Reasoning Reasoning About Knowledge and Action Elements of a Plan-Based Theory of Speech Acts A Truth Maintenance System Generalization as Search Index

Artificial intelligence: engineering, science,

Abstract: This paper presents the view that artificial intelligence (AI) is primarily concerned with propositional languages for representing knowledge and with techniques for manipulating these representations. In this respect, AI is analogous to applied mathematics; its representations and techniques can be applied in a variety of other subject areas. Typically, AI research (or should be) more concerned with the general form and properties of representational languages and methods than it is with the content being described by these languages Notable exceptions involve “commonsense” knowledge about the everyday world (no other specialty claims this subject area as its own), and metaknowledge (or knowledge about the properties and uses of knowledge itself). In these areas AI is concerned with content as well as form. We also observe that the technology that seems to underly peripheral sensory and motor activities (analogous to low-level animal or human vision and muscle control) seems to be quite different from the technology that seems to underly cognitive reasoning and problem solving. Some definitions of AI would include peripheral as well as cognitive processes; here we argue against including the peripheral processes.