William E. Byrd
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Typed Embedding of a Relational Language in Ocaml
Typed Embedding of a Relational Language in OCaml Dmitrii Kosarev Dmitry Boulytchev Saint Petersburg State University Saint Petersburg State University Saint Petersburg, Russia Saint Petersburg, Russia [email protected] [email protected] We present an implementation of the relational programming language miniKanren as a set of combi- nators and syntax extensions for OCaml. The key feature of our approach is polymorphic unification, which can be used to unify data structures of arbitrary types. In addition we provide a useful generic programming pattern to systematically develop relational specifications in a typed manner, and ad- dress the problem of integration of relational subsystems into functional applications. 1 Introduction Relational programming [11] is an attractive technique, based on the idea of constructing programs as relations. As a result, relational programs can be “queried” in various “directions”, making it possible, for example, to simulate reversed execution. Apart from being interesting from a purely theoretical standpoint, this approach may have a practical value: some problems look much simpler when considered as queries to some relational specification [5]. There are a number of appealing examples confirming this observation: a type checker for simply typed lambda calculus (and, at the same time, a type inferencer and solver for the inhabitation problem), an interpreter (capable of generating “quines” — programs producing themselves as a result) [7], list sorting (capable of producing all permutations), etc. Many logic programming languages, such as Prolog, Mercury [21], or Curry [13] to some extent can be considered relational. We have chosen miniKanren1 as a model language, because it was specifically designed as a relational DSL, embedded in Scheme/Racket. -
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. -
Belgium a Logic Meta-Programming Framework for Supporting The
Vrije Universiteit Brussel - Belgium Faculty of Sciences In Collaboration with Ecole des Mines de Nantes - France 2003 ERSITEIT IV B N R U U S E S J I E R L V S C S I E A N R B T E IA N VIN TE CERE ECOLE DES MINES DE NANTES A Logic Meta-Programming Framework for Supporting the Refactoring Process A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science (Thesis research conducted in the EMOOSE exchange) By: Francisca Mu˜nozBravo Promotor: Prof. Dr. Theo D’Hondt (Vrije Universiteit Brussel) Co-Promotors: Dr. Tom Tourw´eand Dr. Tom Mens (Vrije Universiteit Brussel) Abstract The objective of this thesis is to provide automated support for recognizing design flaws in object oriented code, suggesting proper refactorings and performing automatically the ones selected by the user. Software suffers from inevitable changes throughout the development and the maintenance phase, and this usually affects its internal structure negatively. This makes new changes diffi- cult to implement and the code drifts away from the original design. The introduced structural disorder can be countered by applying refactorings, a kind of code transformation that improves the internal structure without affecting the behavior of the application. There are several tools that support applying refactorings in an automated way, but little help for deciding where to apply a refactoring and which refactoring could be applied. This thesis presents an advanced refactoring tool that provides support for the earlier phases of the refactoring process, by detecting and analyzing bad code smells in a software application, proposing appropriate refactorings that solve these smells, and letting the user decide which one to apply. -
Logic Programming Lecture 19 Tuesday, April 5, 2016 1 Logic Programming 2 Prolog
Harvard School of Engineering and Applied Sciences — CS 152: Programming Languages Logic programming Lecture 19 Tuesday, April 5, 2016 1 Logic programming Logic programming has its roots in automated theorem proving. Logic programming differs from theorem proving in that logic programming uses the framework of a logic to specify and perform computation. Essentially, a logic program computes values, using mechanisms that are also useful for deduction. Logic programming typically restricts itself to well-behaved fragments of logic. We can think of logic programs as having two interpretations. In the declarative interpretation, a logic pro- gram declares what is being computed. In the procedural interpretation, a logic program program describes how a computation takes place. In the declarative interpretation, one can reason about the correctness of a program without needing to think about underlying computation mechanisms; this makes declarative programs easier to understand, and to develop. A lot of the time, once we have developed a declarative program using a logic programming language, we also have an executable specification, that is, a procedu- ral interpretation that tells us how to compute what we described. This is one of the appealing features of logic programming. (In practice, executable specifications obtained this way are often inefficient; an under- standing of the underlying computational mechanism is needed to make the execution of the declarative program efficient.) We’ll consider some of the concepts of logic programming by considering the programming language Prolog, which was developed in the early 70s, initially as a programming language for natural language processing. 2 Prolog We start by introducing some terminology and syntax. -
Constraint Microkanren in the Clp Scheme
CONSTRAINT MICROKANREN IN THE CLP SCHEME Jason Hemann Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Doctor of Philosophy in the Department of School of Informatics, Computing, and Engineering Indiana University January 2020 Accepted by the Graduate Faculty, Indiana University, in partial fulfillment of the require- ments for the degree of Doctor of Philosophy. Daniel P. Friedman, Ph.D. Amr Sabry, Ph.D. Sam Tobin-Hochstadt, Ph.D. Lawrence Moss, Ph.D. December 20, 2019 ii Copyright 2020 Jason Hemann ALL RIGHTS RESERVED iii To Mom and Dad. iv Acknowledgements I want to thank all my housemates and friends from 1017 over the years for their care and support. I’m so glad to have you all, and to have you all over the world. Who would have thought that an old house in Bloomington could beget so many great memories. While I’m thinking of it, thanks to Lisa Kamen and Bryan Rental for helping to keep a roof over our head for so many years. Me encantan mis salseros y salseras. I know what happens in the rueda stays in the rueda, so let me just say there’s nothing better for taking a break from right-brain activity. Thanks to Kosta Papanicolau for his early inspiration in math, critical thinking, and research, and to Profs. Mary Flagg and Michael Larsen for subsequent inspiration and training that helped prepare me for this work. Learning, eh?—who knew? I want to thank also my astounding undergraduate mentors including Profs. -
Chapter 11. Knowledge Representation and Reasoning the Quest for Artificial Intelligence, Nilsson, N
Chapter 11. Knowledge Representation and Reasoning The Quest for Artificial Intelligence, Nilsson, N. J., 2009. Lecture Notes on Artificial Intelligence Summarized by Ha, Jung-Woo and Lee, Beom-Jin Biointelligence Laboratory School of Computer Science and Engineering Seoul National Univertisy http://bi.snu.ac.kr Contents 11.1 Deductions in Symbolic Logic Deductions in Symbolic Logic 11.2 The Situation Calculus The Situation Calculus 11.3 Logic Programming Logic Programming 11.4 Semantic Networks Semantic Networks 11.5 Scripts and Frames Scripts and Frames Appendix © 2016, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 2 Overview Methods for knowledge representation and reasoning from Mid-1960s and Mid-1970s Symbolic logic and its deductions Predicate calculus For proving theories Situation calculus Logic programming: PROLOG Sematic networks: HAM, MEMS, MENTAL Script and Frames © 2016, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 3 Introduction Knowledge For intelligent system The mean to draw conclusion from or act on Knowledge representation Procedural Coordinate and control the specific action (ex. hitting a tennis ball) Programs using the knowledge Specific task program Declarative Declarative sentence (I am a 25 years old) Symbolic structures General task program © 2016, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 4 Chapter 11. Knowledge Representation and Reasoning 11.1 Deductions in Symbolic Logic © 2016, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 5 Deductions in Symbolic Logic The predicate calculus From Aristotle to G. Boole and McCarthy Ex. Aristotle syllogism 1. (∀ x)[Man(x) ⊃ Mortal(x)] (The expression “(∀ x)” is a way of writing “for all x”; and the expression “⊃” is a way of writing "implies that." “Man(x)” is a way of writing “x is a man”; and “Mortal(x)” is a way of writing “x is mortal.” Thus, the entire expression is a way of writing “for all x, x is a man implies that x is mortal” or, equivalently, “all men are mortal.”) 2. -
Towards Flexible Goal-Oriented Logic Programming
FACULTY OF SCIENCES Towards Flexible Goal-Oriented Logic Programming ir. Benoit Desouter Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Computer Science Supervisors: prof. dr. ir. Tom Schrijvers prof. dr. ir. Marko van Dooren Department of Applied Mathematics, Computer Science and Statistics Faculty of Sciences, Ghent University ii Acknowledgments As it feels more natural to thank people in the language that we have used on a daily basis during the journey towards completing this PhD thesis, I'll use Dutch for most of the next few pages. In de eerste plaats wil ik mijn promotoren Tom en Marko bedanken. Tom, bedankt voor het geduld als ik occasioneel iets maar half begreep, en om er altijd vertrouwen in te blijven hebben. Je hebt me heel wat kansen aangereikt, waardoor ik van heel wat onderwerpen iets heb opgestoken. Marko, hoewel je er pas halverwege bijkwam, toonde je al snel interesse voor het onderwerp en heb je er vanuit je eigen expertise heel wat aan toegevoegd. Je deur stond altijd voor me open als ik even een tussentijdse statusupdate wou geven. Bedankt voor de babbels over vanalles en nog wat, en om me grondig te betrekken bij het geven van Programmeren 1. Ik heb nog heel wat bijgeleerd over objec- tori¨entatie door jouw visie, slides en codevoorbeelden. Daarnaast ook bedankt om mijn lokale LATEX-goeroe te zijn: niettegenstaande ik LATEX al tien jaar lang gebruik, heb ik voor het precies goed krijgen van deze thesis heel wat nieuwe pakketten en trucjes moeten gebruiken, met regelmatig vreemde out- put of cryptische foutmeldingen tot gevolg die ik niet altijd alleen kon oplossen | of het zou me op zijn minst veel meer tijd gekost hebben. -
Chapter 1 Basic Principles of Programming Languages
Chapter 1 Basic Principles of Programming Languages Although there exist many programming languages, the differences among them are insignificant compared to the differences among natural languages. In this chapter, we discuss the common aspects shared among different programming languages. These aspects include: programming paradigms that define how computation is expressed; the main features of programming languages and their impact on the performance of programs written in the languages; a brief review of the history and development of programming languages; the lexical, syntactic, and semantic structures of programming languages, data and data types, program processing and preprocessing, and the life cycles of program development. At the end of the chapter, you should have learned: what programming paradigms are; an overview of different programming languages and the background knowledge of these languages; the structures of programming languages and how programming languages are defined at the syntactic level; data types, strong versus weak checking; the relationship between language features and their performances; the processing and preprocessing of programming languages, compilation versus interpretation, and different execution models of macros, procedures, and inline procedures; the steps used for program development: requirement, specification, design, implementation, testing, and the correctness proof of programs. The chapter is organized as follows. Section 1.1 introduces the programming paradigms, performance, features, and the development of programming languages. Section 1.2 outlines the structures and design issues of programming languages. Section 1.3 discusses the typing systems, including types of variables, type equivalence, type conversion, and type checking during the compilation. Section 1.4 presents the preprocessing and processing of programming languages, including macro processing, interpretation, and compilation. -
An Introduction to Prolog
Appendix A An Introduction to Prolog A.1 A Short Background Prolog was designed in the 1970s by Alain Colmerauer and a team of researchers with the idea – new at that time – that it was possible to use logic to represent knowledge and to write programs. More precisely, Prolog uses a subset of predicate logic and draws its structure from theoretical works of earlier logicians such as Herbrand (1930) and Robinson (1965) on the automation of theorem proving. Prolog was originally intended for the writing of natural language processing applications. Because of its conciseness and simplicity, it became popular well beyond this domain and now has adepts in areas such as: • Formal logic and associated forms of programming • Reasoning modeling • Database programming • Planning, and so on. This chapter is a short review of Prolog. In-depth tutorials include: in English, Bratko (2012), Clocksin and Mellish (2003), Covington et al. (1997), and Sterling and Shapiro (1994); in French, Giannesini et al. (1985); and in German, Baumann (1991). Boizumault (1988, 1993) contain a didactical implementation of Prolog in Lisp. Prolog foundations rest on first-order logic. Apt (1997), Burke and Foxley (1996), Delahaye (1986), and Lloyd (1987) examine theoretical links between this part of logic and Prolog. Colmerauer started his work at the University of Montréal, and a first version of the language was implemented at the University of Marseilles in 1972. Colmerauer and Roussel (1996) tell the story of the birth of Prolog, including their try-and-fail experimentation to select tractable algorithms from the mass of results provided by research in logic. -
Neural Logic Framework for Digital Assistants
MEng Individual Project Imperial College London Department of Computing Neural Logic Framework for Digital Assistants Supervisor: Prof. Alessandra Russo Author: Nuri Cingillioglu Second Marker: Dr. Krysia Broda June 16, 2017 Abstract Digital assistants are becoming ubiquitous with consumers across mobile platforms help- ing with everyday tasks. The natural language interface of most assistants are built on machine learning based intent parsing techniques. This design cannot handle higher level abstract reasoning such as defaults while logic programs can incorporate them. In this project we present Kevin, a digital personal assistant with a logical framework built on top of neural networks to provide a flexible execution environment while har- nessing the capabilities of machine learning at a lower level. Kevin demonstrates natural language based logical constructs such as unification and resolution with integrated neural network information retrieval. Acknowledgements I would like to thank: • my parents for their wholehearted support, endless love and perpetual encourage- ment. • my supervisor Prof. Alessandra Russo for giving me this opportunity by accepting the project proposal and allowing me to explore the topic as well as her PhD student Mark Law for his feedback. • my personal tutor Dr. Alistair Donaldson for his support and advice for the years. • my friends for their companionship and solace throughout my degree. Contents 1 Introduction 11 1.1 Motivation.................................... 11 1.2 Objectives.................................... 12 1.3 Challenges.................................... 12 1.4 Contributions.................................. 13 2 Related Work 14 2.1 Natural Language Interactions......................... 14 2.1.1 Turing Test............................... 14 2.1.2 Conversational Agents......................... 15 2.2 Domain-specific Assistants........................... 17 2.2.1 PANDA: Virtual assistant for in-car child entertainment..... -
CMSC 330: Organization of Programming Languages
CMSC 330: Organization of Programming Languages Administrivia CMSC330 Spring 2020 1 Course Goals Understand why there are so many languages Describe and compare their main features Choose the right language for the job Write better code • Code that is shorter, more efficient, with fewer bugs In short: • Become a better programmer with a better understanding of your tools. CMSC330 Spring 2020 2 Course Activities Learn different types of languages Learn different language features and tradeoffs • Programming patterns repeat between languages Study how languages are specified • Syntax, Semantics — mathematical formalisms Study how languages are implemented • Parsing via regular expressions (automata theory) and context free grammars • Mechanisms such as closures, tail recursion, lazy evaluation, garbage collection, … Language impact on computer security CMSC330 Spring 2020 3 Syllabus Dynamic/ Scripting languages (Ruby) Functional programming (OCaml) Regular expressions & finite automata Context-free grammars & parsing Lambda Calculus Safe, “zero-cost abstraction” programming (Rust) Secure programming Scoping, type systems, parameter passing, Comparing language styles; other topics CMSC330 Spring 2020 4 Calendar / Course Overview Tests • 4 quizzes, 2 midterm exams, 1 final exam • Do not schedule your interviews on exam dates Clicker quizzes • In class, graded, during the lectures Projects • Project 1 – Ruby • Project 2-5 – OCaml (and parsing, automata) Ø P2 and P4 are split in two parts • Project 6 – Security CMSC330 Spring 2020 5 Clickers Turning Technology subscription is free. Physical clicker is preferred. • Clicker device: any of LCD, NXT, or QT2 models • Phone App: needs wifi CMSC330 Spring 2020 6 Quiz time! According to IEEE Spectrum Magazine which is the “top” programming language of 2018? A. -
9B. Combining Logic Programs with Object-Oriented Representation
9b. Combining Logic Programs with Object-Oriented Representation Outline • Limitations of frames & description logics • Early hybrid KR&R systems – Krypton • Recent trends in hybrid KR&R systems – Description graphs – F-Logic Limitation of Frames & Description Logics • Polyadic predicates – Relations with arity greater than two • A is between B and C • Limited relational expressivity – DLs can model only domains where objects are connected in a tree like manner • ``an uncle of a person is a brother of that person’s father’’ • Disjunction – Many concepts have disjunctively defined definitions • A person is legally employable if they are of employable age, and they are either a legal immigrant or US citizen • Negation and relative complements – Many facts are inherently negative • Under no circumstances is it illegal to drive on the shoulder of a road • Concepts defined using conditionals – An animal is dangerous to humans if it attacks them when near by • Equivalence to two other concepts – A dinner wine is proper if it is red whenever the main dish is red meat or is tomato based or else if it is white wine A more complete list is available in ``Two theses of knowledge representation: language restrictions, taxonomic classification, and the utility of representation services’’ by Doyle and Patil, AIJ, 1991 Krypton • Knowledge representation has two components – Terminological component (Tbox) – Assertional component (ABox) • Functional specification of a Knowledge Base – Instead of focusing on ``What structures should the system maintain