Publisher Summary This chapter discusses Answer Set Prolog, which is a language for knowledge representation and reasoning based on the answer set/stable model semantics of logic programs. The language has roots in the declarative programming, syntax, and semantics of standard Prolog, disjunctive databases, and nonmonotonic logic. Unlike standard Prolog, it allows expressing disjunction and classic or strong negation. It differs from many other knowledge representation languages by its ability to represent defaults. A substantial part of education consists in learning various defaults, exceptions to these defaults, and the ways of using this information to draw reasonable conclusions about the world and the consequences of one's actions. Answer Set Prolog provides a powerful logical model of this process. Its syntax allows a simple representation of defaults and their exceptions, its consequence relation characterizes the corresponding set of valid conclusions, and its inference mechanisms allow a program to find these conclusions in a reasonable amount of time.

Chapter 7 Answer Sets

CORAL is a modular declarative query language/programming language that supports general Horn clauses with complex terms, set-grouping, aggregation, negation, and relations with tuples that contain (universally quantified) variables. Support for persistent relations is provided by using the EXODUS storage manager. A unique feature of CORAL is that it provides a wide range of evaluation strategies and allows users to optionally tailor execution of a program through high-level annotations. A CORAL program is organized as a collection of modules, and this structure is used as the basis for expressing control choices. CORAL has an interface to C++, and uses the class structure of C++ to provide extensibility. FinaUy, CORAL supports a command sublanguage, in which statements are evaluated in a user-specified order. The statements can be queries, updates, production-system style rules, or any command that can be typed in at the CORAL

/pdf/coral-control-relations-and-logic-223kn2i271.pdf

CORAL - Control, Relations and Logic

Review - Magic Sets and Other Strange Ways to Implement Logic Programs.

The area of deductive databases has matured in recent years, and it now seems appropriate to reflect upon what has been achieved and what the future holds. In this paper, we provide an overview of the area and briefly describe a number of projects that have led to implemented systems.

A Survey of Research in Deductive Database Systems

The area of deductive databases has matured in recent years and it now seems appropriate to re ect upon what has been achieved and what the future holds In this paper we provide an overview of the area and brie y describe a number of projects that have led to implemented systems

A Survey of Research on Deductive Database Systems

An overview of the structure of Aditi, a disk-based deductive database system under continuous development at the University of Melbourne, is presented. The aim of the project is to find out what implementation methods and optimization techniques would make deductive databases competitive with current commercial relational databases. The structure of the Aditi prototype is based on a variant of the client-server model. The front end of Aditi interacts with the user exclusively in a logical language that has more expressive power than relational query languages. The back end uses relational technology for efficiency in the management of disk-based data and uses some optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The system has been functional for almost two years now, and has already proven its worth as a research tool. >

Design Overview of the Aditi Deductive Database System.

Design overview of the Aditi deductive database system

The Aditi Deductive Datebase System.

An introduction to Aditi deductive database system

A Stream AND-Parallel Execution Algorithm with Backtracking.

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