An Embedding of the Relational Paradigm in an Array Language with Rank-Polymorphic Unification
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Answer Set Programming: a Tour from the Basics to Advanced Development Tools and Industrial Applications
Answer Set Programming: A tour from the basics to advanced development tools and industrial applications Nicola Leone and Francesco Ricca Department of Mathematics and Computer Science, University of Calabria, Italy leone,ricca @mat.unical.it { } Abstract. Answer Set Programming (ASP) is a powerful rule-based language for knowledge representation and reasoning that has been developed in the field of logic programming and nonmonotonic reasoning. After more than twenty years from the introduction of ASP, the theoretical properties of the language are well understood and the solving technology has become mature for practical appli- cations. In this paper, we first present the basics of the ASP language, and we then concentrate on its usage for knowledge representation and reasoning in real- world contexts. In particular, we report on the development of some industry-level applications with the ASP system DLV, and we illustrate two advanced develop- ment tools for ASP, namely ASPIDE and JDLV, which speed-up and simplify the implementation of applications. 1 Introduction Answer Set Programming (ASP) [11, 19, 30] is a powerful rule-based language for knowledge representation and reasoning that has been developed in the field of logic programming and nonmonotonic reasoning. ASP features disjunction in rule heads, non monotonic negation in rule bodies [30], aggregate atoms [16] for concise mod- eling of complex combinatorial problems, and weak constraints [12] for the declarative encoding of optimization problems. Computational problems, even of high complexity [19], can be solved in ASP by specifying a logic program, i.e., a set of logic rules, such that its answer sets correspond to solutions, and then, using an answer set solver to find such solutions [38, 34]. -
R from a Programmer's Perspective Accompanying Manual for an R Course Held by M
DSMZ R programming course R from a programmer's perspective Accompanying manual for an R course held by M. Göker at the DSMZ, 11/05/2012 & 25/05/2012. Slightly improved version, 10/09/2012. This document is distributed under the CC BY 3.0 license. See http://creativecommons.org/licenses/by/3.0 for details. Introduction The purpose of this course is to cover aspects of R programming that are either unlikely to be covered elsewhere or likely to be surprising for programmers who have worked with other languages. The course thus tries not be comprehensive but sort of complementary to other sources of information. Also, the material needed to by compiled in short time and perhaps suffers from important omissions. For the same reason, potential participants should not expect a fully fleshed out presentation but a combination of a text-only document (this one) with example code comprising the solutions of the exercises. The topics covered include R's general features as a programming language, a recapitulation of R's type system, advanced coding of functions, error handling, the use of attributes in R, object-oriented programming in the S3 system, and constructing R packages (in this order). The expected audience comprises R users whose own code largely consists of self-written functions, as well as programmers who are fluent in other languages and have some experience with R. Interactive users of R without programming experience elsewhere are unlikely to benefit from this course because quite a few programming skills cannot be covered here but have to be presupposed. -
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
Parallel Functional Programming with APL
Parallel Functional Programming, APL Lecture 1 Parallel Functional Programming with APL Martin Elsman Department of Computer Science University of Copenhagen DIKU December 19, 2016 Martin Elsman (DIKU) Parallel Functional Programming, APL Lecture 1 December 19, 2016 1 / 18 Outline 1 Outline Course Outline 2 Introduction to APL What is APL APL Implementations and Material APL Scalar Operations APL (1-Dimensional) Vector Computations Declaring APL Functions (dfns) APL Multi-Dimensional Arrays Iterations Declaring APL Operators Function Trains Examples Reading Martin Elsman (DIKU) Parallel Functional Programming, APL Lecture 1 December 19, 2016 2 / 18 Outline Course Outline Teachers Martin Elsman (ME), Ken Friis Larsen (KFL), Andrzej Filinski (AF), and Troels Henriksen (TH) Location Lectures in Small Aud, Universitetsparken 1 (UP1); Labs in Old Library, UP1 Course Description See http://kurser.ku.dk/course/ndak14009u/2016-2017 Course Outline Week 47 48 49 50 51 1–3 Mon 13–15 Intro, Futhark Parallel SNESL APL (ME) Project Futhark (ME) Haskell (AF) (ME) (KFL) Mon 15–17 Lab Lab Lab Lab Project Wed 13–15 Futhark Parallel SNESL Invited APL Project (ME) Haskell (AF) Lecture (ME) / (KFL) (John Projects Reppy) Martin Elsman (DIKU) Parallel Functional Programming, APL Lecture 1 December 19, 2016 3 / 18 Introduction to APL What is APL APL—An Ancient Array Programming Language—But Still Used! Pioneered by Ken E. Iverson in the 1960’s. E. Dijkstra: “APL is a mistake, carried through to perfection.” There are quite a few APL programmers around (e.g., HIPERFIT partners). Very concise notation for expressing array operations. Has a large set of functional, essentially parallel, multi- dimensional, second-order array combinators. -
Complexity Results for Probabilistic Answer Set Programming
International Journal of Approximate Reasoning 118 (2020) 133–154 Contents lists available at ScienceDirect International Journal of Approximate Reasoning www.elsevier.com/locate/ijar Complexity results for probabilistic answer set programming ∗ Denis Deratani Mauá a, , Fabio Gagliardi Cozman b a Institute of Mathematics and Statistics, Universidade de São Paulo, Brazil b Escola Politécnica, Universidade de São Paulo, Brazil a r t i c l e i n f o a b s t r a c t Article history: We analyze the computational complexity of probabilistic logic programming with Received 16 May 2019 constraints, disjunctive heads, and aggregates such as sum and max. We consider Received in revised form 25 October 2019 propositional programs and relational programs with bounded-arity predicates, and look Accepted 9 December 2019 at cautious reasoning (i.e., computing the smallest probability of an atom over all Available online 16 December 2019 probability models), cautious explanation (i.e., finding an interpretation that maximizes the Keywords: lower probability of evidence) and cautious maximum-a-posteriori (i.e., finding a partial Probabilistic logic programming interpretation for a set of atoms that maximizes their lower probability conditional on Answer set programming evidence) under Lukasiewicz’s credal semantics. Computational complexity © 2019 Elsevier Inc. All rights reserved. 1. Introduction Probabilities and logic programming have been combined in a variety of ways [1–8]. A particularly interesting and power- ful combination is offered by probabilistic answer set programming, which exploits the powerful knowledge representation and problem solving toolset of answer set programming [9]. Available surveys describe probabilistic logic programming in detail and go over many promising applications [10–13]. -
Answer Set Programming: a Primer?
Answer Set Programming: A Primer? Thomas Eiter1, Giovambattista Ianni2, and Thomas Krennwallner1 1 Institut fur¨ Informationssysteme, Technische Universitat¨ Wien Favoritenstraße 9-11, A-1040 Vienna, Austria feiter,[email protected] 2 Dipartimento di Matematica, Universita´ della Calabria, I-87036 Rende (CS), Italy [email protected] Abstract. Answer Set Programming (ASP) is a declarative problem solving para- digm, rooted in Logic Programming and Nonmonotonic Reasoning, which has been gaining increasing attention during the last years. This article is a gentle introduction to the subject; it starts with motivation and follows the historical development of the challenge of defining a semantics for logic programs with negation. It looks into positive programs over stratified programs to arbitrary programs, and then proceeds to extensions with two kinds of negation (named weak and strong negation), and disjunction in rule heads. The second part then considers the ASP paradigm itself, and describes the basic idea. It shows some programming techniques and briefly overviews Answer Set solvers. The third part is devoted to ASP in the context of the Semantic Web, presenting some formalisms and mentioning some applications in this area. The article concludes with issues of current and future ASP research. 1 Introduction Over the the last years, Answer Set Programming (ASP) [1–5] has emerged as a declar- ative problem solving paradigm that has its roots in Logic Programming and Non- monotonic Reasoning. This particular way of programming, in a language which is sometimes called AnsProlog (or simply A-Prolog) [6, 7], is well-suited for modeling and (automatically) solving problems which involve common sense reasoning: it has been fruitfully applied to a range of applications (for more details, see Section 6). -
Functional and Logic Programming - Wolfgang Schreiner
COMPUTER SCIENCE AND ENGINEERING - Functional and Logic Programming - Wolfgang Schreiner FUNCTIONAL AND LOGIC PROGRAMMING Wolfgang Schreiner Research Institute for Symbolic Computation (RISC-Linz), Johannes Kepler University, A-4040 Linz, Austria, [email protected]. Keywords: declarative programming, mathematical functions, Haskell, ML, referential transparency, term reduction, strict evaluation, lazy evaluation, higher-order functions, program skeletons, program transformation, reasoning, polymorphism, functors, generic programming, parallel execution, logic formulas, Horn clauses, automated theorem proving, Prolog, SLD-resolution, unification, AND/OR tree, constraint solving, constraint logic programming, functional logic programming, natural language processing, databases, expert systems, computer algebra. Contents 1. Introduction 2. Functional Programming 2.1 Mathematical Foundations 2.2 Programming Model 2.3 Evaluation Strategies 2.4 Higher Order Functions 2.5 Parallel Execution 2.6 Type Systems 2.7 Implementation Issues 3. Logic Programming 3.1 Logical Foundations 3.2. Programming Model 3.3 Inference Strategy 3.4 Extra-Logical Features 3.5 Parallel Execution 4. Refinement and Convergence 4.1 Constraint Logic Programming 4.2 Functional Logic Programming 5. Impacts on Computer Science Glossary BibliographyUNESCO – EOLSS Summary SAMPLE CHAPTERS Most programming languages are models of the underlying machine, which has the advantage of a rather direct translation of a program statement to a sequence of machine instructions. Some languages, however, are based on models that are derived from mathematical theories, which has the advantages of a more concise description of a program and of a more natural form of reasoning and transformation. In functional languages, this basis is the concept of a mathematical function which maps a given argument values to some result value. -
Programming Language
Programming language A programming language is a formal language, which comprises a set of instructions that produce various kinds of output. Programming languages are used in computer programming to implement algorithms. Most programming languages consist of instructions for computers. There are programmable machines that use a set of specific instructions, rather than general programming languages. Early ones preceded the invention of the digital computer, the first probably being the automatic flute player described in the 9th century by the brothers Musa in Baghdad, during the Islamic Golden Age.[1] Since the early 1800s, programs have been used to direct the behavior of machines such as Jacquard looms, music boxes and player pianos.[2] The programs for these The source code for a simple computer program written in theC machines (such as a player piano's scrolls) did not programming language. When compiled and run, it will give the output "Hello, world!". produce different behavior in response to different inputs or conditions. Thousands of different programming languages have been created, and more are being created every year. Many programming languages are written in an imperative form (i.e., as a sequence of operations to perform) while other languages use the declarative form (i.e. the desired result is specified, not how to achieve it). The description of a programming language is usually split into the two components ofsyntax (form) and semantics (meaning). Some languages are defined by a specification document (for example, theC programming language is specified by an ISO Standard) while other languages (such as Perl) have a dominant implementation that is treated as a reference. -
RAL-TR-1998-060 Co-Array Fortran for Parallel Programming
RAL-TR-1998-060 Co-Array Fortran for parallel programming 1 by R. W. Numrich23 and J. K. Reid Abstract Co-Array Fortran, formerly known as F−− , is a small extension of Fortran 95 for parallel processing. A Co-Array Fortran program is interpreted as if it were replicated a number of times and all copies were executed asynchronously. Each copy has its own set of data objects and is termed an image. The array syntax of Fortran 95 is extended with additional trailing subscripts in square brackets to give a clear and straightforward representation of any access to data that is spread across images. References without square brackets are to local data, so code that can run independently is uncluttered. Only where there are square brackets, or where there is a procedure call and the procedure contains square brackets, is communication between images involved. There are intrinsic procedures to synchronize images, return the number of images, and return the index of the current image. We introduce the extension; give examples to illustrate how clear, powerful, and flexible it can be; and provide a technical definition. Categories and subject descriptors: D.3 [PROGRAMMING LANGUAGES]. General Terms: Parallel programming. Additional Key Words and Phrases: Fortran. Department for Computation and Information, Rutherford Appleton Laboratory, Oxon OX11 0QX, UK August 1998. 1 Available by anonymous ftp from matisa.cc.rl.ac.uk in directory pub/reports in the file nrRAL98060.ps.gz 2 Silicon Graphics, Inc., 655 Lone Oak Drive, Eagan, MN 55121, USA. Email: -
Programming the Capabilities of the PC Have Changed Greatly Since the Introduction of Electronic Computers
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in INTRODUCTION TO PROGRAMMING LANGUAGE Topic Objective: At the end of this topic the student will be able to understand: History of Computer Programming C++ Definition/Overview: Overview: A personal computer (PC) is any general-purpose computer whose original sales price, size, and capabilities make it useful for individuals, and which is intended to be operated directly by an end user, with no intervening computer operator. Today a PC may be a desktop computer, a laptop computer or a tablet computer. The most common operating systems are Microsoft Windows, Mac OS X and Linux, while the most common microprocessors are x86-compatible CPUs, ARM architecture CPUs and PowerPC CPUs. Software applications for personal computers include word processing, spreadsheets, databases, games, and myriad of personal productivity and special-purpose software. Modern personal computers often have high-speed or dial-up connections to the Internet, allowing access to the World Wide Web and a wide range of other resources. Key Points: 1. History of ComputeWWW.BSSVE.INr Programming The capabilities of the PC have changed greatly since the introduction of electronic computers. By the early 1970s, people in academic or research institutions had the opportunity for single-person use of a computer system in interactive mode for extended durations, although these systems would still have been too expensive to be owned by a single person. The introduction of the microprocessor, a single chip with all the circuitry that formerly occupied large cabinets, led to the proliferation of personal computers after about 1975. Early personal computers - generally called microcomputers - were sold often in Electronic kit form and in limited volumes, and were of interest mostly to hobbyists and technicians. -
Answer Set Programming
ANSWER SET PROGRAMMING Tran Cao Son Department of Computer Science New Mexico State University Las Cruces, NM 88011, USA [email protected] http://www.cs.nmsu.edu/~tson October 2005 Answer Set Programming. Acknowledgement This tutorial contains some materials from tutorials on answer set programming and on knowledge representation and logic programming from those provided by • Chitta Baral, available at www.public.asu.edu/~cbaral. • Michael Gelfond, available at www.cs.ttu.ued/~mgelfond. Tran Cao Son 1 Answer Set Programming. Introduction — Answer Set Programming Answer set programming is a new programming paradigm. It is introduced in the late 90’s and manages to attracts the intention of different groups of researchers thanks to its: • declarativeness: programs do not specify how answers are computed; • modularity: programs can be developed incrementally; • expressiveness: answer set programming can be used to solve problems in high 2 complexity classes (e.g. ΣP , Π2P , etc.) Answer set programming has been applied in several areas: reasoning about actions and changes, planning, configuration, wire routing, phylogenetic inference, semantic web, information integration, etc. Tran Cao Son 2 Answer Set Programming. Purpose • Introduce answer set programming • Provide you with some initial references, just in case • ...you get excited about answer set programming Tran Cao Son 3 Answer Set Programming. Outline • Foundation of answer set programming: logic programming with answer set semantics (syntax, semantics, early application). • Answer set programming: general ideas and examples • Application of answer set programming in – Knowledge representation – Constraint satisfaction problem – Combinatoric problems – Reasoning about action and change – Planning and diagnostic reasoning • Current issues Tran Cao Son 4 LOGIC PROGRAMMING AND ANSWER SET SEMANTICS Answer Set Programming. -
A Meta-Programming Technique for Debugging Answer-Set Programs∗
A Meta-Programming Technique for Debugging Answer-Set Programs∗ Martin Gebser1, Jorg¨ Puhrer¨ 2, Torsten Schaub1, and Hans Tompits2 1 Institut fur¨ Informatik, Universitat¨ Potsdam, Germany, {gebser,torsten}@cs.uni-potsdam.de 2 Institut fur¨ Informationssysteme 184/3, Technische Universitat¨ Wien, Austria, {puehrer,tompits}@kr.tuwien.ac.at Abstract applying such an approach to ASP results in some deci- sive drawbacks, undermining the declarativity of answer- Answer-set programming (ASP) is widely recognised as a vi- able tool for declarative problem solving. However, there is set semantics. In particular, establishing canonical tracing- currently a lack of tools for developing answer-set programs. based techniques for debugging answer-set programs re- In particular, providing tools for debugging answer-set pro- quires also a canonical procedure for answer-set computa- grams has recently been identified as a crucial prerequisite tion, and programmers would have to be familiar with the al- for a wider acceptance of ASP. In this paper, we introduce a gorithm. This would lead to a shift of perspectives in which meta-programming technique for debugging in ASP. The ba- an answer-set program is degraded from a declarative prob- sic question we address is why interpretations expected to be lem description to a set of parameters for a static solving answer sets are not answer sets of the program to debug. We algorithm. Therefore, we argue for declarative debugging thus deal with finding semantical errors of programs. The ex- strategies that are independent of answer-set computation. planations provided by our method are based on an intuitive Indeed, among the few approaches dealing with debug- scheme of errors that relies on a recent characterisation of the answer-set semantics.