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.

Deductive databases result from the integration of relational database and logic programming techniques. However, significant problems remain inherent in this simple synthesis from the language point of view. In this paper, we discuss these problems from four different aspects: complex values, object orientation, higher-orderness, and updates. In each case, we examine four typical languages that address the corresponding issues.

/pdf/deductive-database-languages-problems-and-solutions-1cf428iw6m.pdf

Deductive database languages: problems and solutions

A very desirable Datalog extension investigated by many researchers in the last 30 years consists in allowing the use of the basic SQL aggregates min, max, count and sum in recursive rules. In this paper, we propose a simple comprehensive solution that extends the declarative least-fixpoint semantics of Horn Clauses, along with the optimization techniques used in the bottom-up implementation approach adopted by many Datalog systems. We start by identifying a large class of programs of great practical interest in which the use of min or max in recursive rules does not compromise the declarative fixpoint semantics of the programs using those rules. Then, we revisit the monotonic versions of count and sum aggregates proposed by Mazuran et al. (2013b, The VLDB Journal 22, 4, 471–493) and named, respectively, mcount and msum. Since mcount, and also msum on positive numbers, are monotonic in the lattice of set-containment, they preserve the fixpoint semantics of Horn Clauses. However, in many applications of practical interest, their use can lead to inefficiencies, that can be eliminated by combining them with max, whereby mcount and msum become the standard count and sum. Therefore, the semantics and optimization techniques of Datalog are extended to recursive programs with min, max, count and sum, making possible the advanced applications of superior performance and scalability demonstrated by BigDatalog (Shkapsky et al. 2016. In SIGMOD. ACM, 1135–1149) and Datalog-MC (Yang et al. 2017. The VLDB Journal 26, 2, 229–248).

pdf/fixpoint-semantics-and-optimization-of-recursive-datalog-b77dcyoawi.pdf

Fixpoint Semantics and Optimization of Recursive Datalog Programs with Aggregates

The Aditi Deductive Datebase System.

The Tenth International Conference on Logic Programming, sponsored by the Association for Logic Programming, is a major forum for presentations of research, applications, and implementations in this important area of computer science. Logic programming is one of the most promising steps toward declarative programming and forms the theoretical basis of the programming language Prolog and it svarious extensions. Logic programming is also fundamental to work in artificial intelligence, where it has been used for nonmonotonic and commonsense reasoning, expert systems implementation, deductive databases, and applications such as computer-aided manufacturing.David S. Warren is Professor of Computer Science at the State University of New York, Stony Brook.Topics covered: Theory and Foundations. Programming Methodologies and Tools. Meta and Higher-order Programming. Parallelism. Concurrency. Deductive Databases. Implementations and Architectures. Applications. Artificial Intelligence. Constraints. Partial Deduction. Bottom-Up Evaluation. Compilation Techniques.

Status of the Aditi deductive database system

Deductive databases generalize relational databases by providing support for recursive views and non-atomic data. Aditi is a deductive system based on the client-server model; it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses relational technology for efficiency in the management of disk-based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.

/pdf/the-aditi-deductive-database-system-4ti6bxri6j.pdf

The aditi deductive database system

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Tim S. Leask

Author Tools

Chat about Author

Papers

The Aditi Deductive Datebase System.

Status of the Aditi deductive database system

The aditi deductive database system