Synopses & Reviews
This exciting new text reveals both the evolution of this programming paradigm since its inception and the impressively broad scope of current research in the field. The contributors to this book are all leading world experts in Logic Programming, and they deal with both theoretical and practical issues. They address such diverse topics as: computational molecular biology, machine learning, mobile computing, multi-agent systems, planning, numerical computing and dynamical systems, database systems, an alternative to the "formulas as types" approach, program semantics and analysis, and natural language processing. XXXXXXX Neuer Text Logic Programming was founded 25 years ago. This exciting book reveals both the evolution of this programming paradigm and its impressively broad scope of current research. The contributions by leading computer scientists deal with both theoretical and practical issues. They address diverse topics such as: computational molecular biology, machine learning, mobile computing, multi-agent systems, numerical computing and dynamical systems, database systems, program semantics, natural language processing, and promising future directions.
Synopsis
Logic Programming was founded 25 years ago. This exciting new text reveals both the evolution of this programming paradigm since its inception and the impressively broad scope of current research in Logic Programming. The contributions to the book deal with both theoretical and practical issues. They address such diverse topics as: computational molecular biology, machine learning, mobile computing, multi-agent systems, planning, numerical computing and dynamical systems, database systems, an alternative to the "formulas as types" approach, program semantics and analysis, and natural language processing. The contributors are all leading world experts in Logic Programming and their contributions were all invited and refereed.
Table of Contents
Preface.- Part I. Computing and Programming: Concurrent and Agent Programming. Program Analysis and Methodology. Future of Declarative Programming. Continuous Mathematics.- Part II. Knowledge Representation and Modeling: Constraints; Machine Learning; Answer Set Programming; Database Systems; Natural Language Processing.