Genesereth, Michael.
Introduction to Logic Programming [electronic resource] / by Michael Genesereth, Vinay K. Chaudhri. - 1st ed. 2020. - XX, 199 p. online resource. - Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616 . - Synthesis Lectures on Artificial Intelligence and Machine Learning, .
Preface -- Introduction -- Datasets -- Queries -- Updates -- Query Evaluation -- View Optimization -- View Definitions -- View Evaluation -- Examples -- Lists, Sets, Trees -- Dynamic Systems -- Metaknowledge -- Operations -- Dynamic Logic Programs -- Database Management -- Interactive Worksheets -- Variations -- References -- Authors' Biographies .
Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic. Over the years, there has been growing interest in Logic Programming due to applications in deductive databases, automated worksheets, Enterprise Management (business rules), Computational Law, and General Game Playing. This book introduces Logic Programming theory, current technology, and popular applications. In this volume, we take an innovative, model-theoretic approach to logic programming. We begin with the fundamental notion of datasets, i.e., sets of ground atoms. Given this fundamental notion, we introduce views, i.e., virtual relations; and we define classical logic programs as sets of view definitions, written using traditional Prolog-like notation but with semantics given in terms of datasets rather than implementation. We then introduce actions, i.e., additions and deletions of ground atoms; and we define dynamic logic programs as sets of action definitions. In addition to the printed book, there is an online version of the text with an interpreter and a compiler for the language used in the text and an integrated development environment for use in developing and deploying practical logic programs.
9783031015861
10.1007/978-3-031-01586-1 doi
Artificial intelligence.
Machine learning.
Neural networks (Computer science) .
Artificial Intelligence.
Machine Learning.
Mathematical Models of Cognitive Processes and Neural Networks.
Q334-342 TA347.A78
006.3
Introduction to Logic Programming [electronic resource] / by Michael Genesereth, Vinay K. Chaudhri. - 1st ed. 2020. - XX, 199 p. online resource. - Synthesis Lectures on Artificial Intelligence and Machine Learning, 1939-4616 . - Synthesis Lectures on Artificial Intelligence and Machine Learning, .
Preface -- Introduction -- Datasets -- Queries -- Updates -- Query Evaluation -- View Optimization -- View Definitions -- View Evaluation -- Examples -- Lists, Sets, Trees -- Dynamic Systems -- Metaknowledge -- Operations -- Dynamic Logic Programs -- Database Management -- Interactive Worksheets -- Variations -- References -- Authors' Biographies .
Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic. Over the years, there has been growing interest in Logic Programming due to applications in deductive databases, automated worksheets, Enterprise Management (business rules), Computational Law, and General Game Playing. This book introduces Logic Programming theory, current technology, and popular applications. In this volume, we take an innovative, model-theoretic approach to logic programming. We begin with the fundamental notion of datasets, i.e., sets of ground atoms. Given this fundamental notion, we introduce views, i.e., virtual relations; and we define classical logic programs as sets of view definitions, written using traditional Prolog-like notation but with semantics given in terms of datasets rather than implementation. We then introduce actions, i.e., additions and deletions of ground atoms; and we define dynamic logic programs as sets of action definitions. In addition to the printed book, there is an online version of the text with an interpreter and a compiler for the language used in the text and an integrated development environment for use in developing and deploying practical logic programs.
9783031015861
10.1007/978-3-031-01586-1 doi
Artificial intelligence.
Machine learning.
Neural networks (Computer science) .
Artificial Intelligence.
Machine Learning.
Mathematical Models of Cognitive Processes and Neural Networks.
Q334-342 TA347.A78
006.3