A Meta-Programming Approach to Realizing Dependently Typed Logic Programming
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Foundational Proof Checkers with Small Witnesses
Foundational Proof Checkers with Small Witnesses Dinghao Wu Andrew W. Appel Aaron Stump Princeton University Washington University in St. Louis dinghao,appel @cs.princeton.edu [email protected] f g ABSTRACT tion of the proof witness to the size of the binary machine- Proof checkers for proof-carrying code (and similar) systems language program. Necula's LFi proof witnesses were about can suffer from two problems: huge proof witnesses and un- 4 times as big as the programs whose properties they proved. trustworthy proof rules. No previous design has addressed both of these problems simultaneously. We show the theory, Pfenning's Elf and Twelf systems [14, 15] are implementa- design, and implementation of a proof-checker that permits tions of the Edinburgh Logical Framework. In these sys- small proof witnesses and machine-checkable proofs of the tems, proof-search engines can be represented as logic pro- soundness of the system. grams, much like (dependently typed, higher-order) Prolog. Elf and Twelf build proof witnesses automatically; if each logic-program clause is viewed as an inference rule, then the 1. INTRODUCTION proof witness is a trace of the successful goals and subgoals In a proof-carrying code (PCC) system [10], or in other executed by the logic program. That is, the proof witness proof-carrying applications [3], an untrusted prover must is a tree whose nodes are the names of clauses and whose convince a trusted checker of the validity of a theorem by subtrees correspond to the subgoals of these clauses. sending a proof. Two of the potential problems with this approach are that the proofs might be too large, and that Necula's theorem provers were written in this style, origi- the checker might not be trustworthy. -
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. -
Functions-As-Constructors Higher-Order Unification
Functions-as-constructors Higher-order Unification Tomer Libal and Dale Miller Inria Saclay & LIX/École Polytechnique, Palaiseau, France Abstract Unification is a central operation in the construction of a range of computational logic systems based on first-order and higher-order logics. First-order unification has a number of properties that dominates the way it is incorporated within such systems. In particular, first-order unifi- cation is decidable, unary, and can be performed on untyped term structures. None of these three properties hold for full higher-order unification: unification is undecidable, unifiers can be incomparable, and term-level typing can dominate the search for unifiers. The so-called pattern subset of higher-order unification was designed to be a small extension to first-order unification that respected the basic laws governing λ-binding (the equalities of α, β, and η-conversion) but which also satisfied those three properties. While the pattern fragment of higher-order unification has been popular in various implemented systems and in various theoretical considerations, it is too weak for a number of applications. In this paper, we define an extension of pattern unifica- tion that is motivated by some existing applications and which satisfies these three properties. The main idea behind this extension is that the arguments to a higher-order, free variable can be more than just distinct bound variables: they can also be terms constructed from (sufficient numbers of) such variables using term constructors and where no argument is a subterm of any other argument. We show that this extension to pattern unification satisfies the three properties mentioned above. -
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. -
A Survey of Engineering of Formally Verified Software
The version of record is available at: http://dx.doi.org/10.1561/2500000045 QED at Large: A Survey of Engineering of Formally Verified Software Talia Ringer Karl Palmskog University of Washington University of Texas at Austin [email protected] [email protected] Ilya Sergey Milos Gligoric Yale-NUS College University of Texas at Austin and [email protected] National University of Singapore [email protected] Zachary Tatlock University of Washington [email protected] arXiv:2003.06458v1 [cs.LO] 13 Mar 2020 Errata for the present version of this paper may be found online at https://proofengineering.org/qed_errata.html The version of record is available at: http://dx.doi.org/10.1561/2500000045 Contents 1 Introduction 103 1.1 Challenges at Scale . 104 1.2 Scope: Domain and Literature . 105 1.3 Overview . 106 1.4 Reading Guide . 106 2 Proof Engineering by Example 108 3 Why Proof Engineering Matters 111 3.1 Proof Engineering for Program Verification . 112 3.2 Proof Engineering for Other Domains . 117 3.3 Practical Impact . 124 4 Foundations and Trusted Bases 126 4.1 Proof Assistant Pre-History . 126 4.2 Proof Assistant Early History . 129 4.3 Proof Assistant Foundations . 130 4.4 Trusted Computing Bases of Proofs and Programs . 137 5 Between the Engineer and the Kernel: Languages and Automation 141 5.1 Styles of Automation . 142 5.2 Automation in Practice . 156 The version of record is available at: http://dx.doi.org/10.1561/2500000045 6 Proof Organization and Scalability 162 6.1 Property Specification and Encodings . -
Efficient Verification Techniques For
Overcoming performance barries: efficient verification techniques for logical frameworks Brigitte Pientka School of Computer Science McGill University Montreal, Canada [email protected] Abstract. In recent years, logical frameworks which support formalizing language specifications to- gether with their meta-theory have been pervasively used in small and large-scale applications, from certifying code [2] to advocating a general infrastructure for formalizing the meta-theory and semantics of programming languages [5]. In particular, the logical framework LF [9], based on the dependently typed lambda-calculus, and light-weight variants of it like LFi [17] have played a major role in these applications. While the acceptance of logical framework technology has grown and they have matured, one of the most criticized points is concerned with the run-time performance. In this tutorial we give a brief introduction to logical frameworks, describe its state-of-the art and present recent advances in addressing some of the existing performance issues. 1 Introduction Logical frameworks [9] provide an experimental platform to specify, and implement formal systems together with the proofs about them. One of its applications lies in proof-carrying code, where it is successfully used to specify and verify formal guarantees about the run-time behavior of programs. More generally, logical frameworks provide an elegant language for encoding and mechanizing the meta-theory and semantics of programming languages. This idea has recently found strong advocates among programming languages researchers who proposed the POPLmark challenge, a set of benchmarks to evaluate and compare tool support to experiment with programming language design, and mechanize the reasoning about programming languages [5]. -
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. -
Type Theory and Applications
Type Theory and Applications Harley Eades [email protected] 1 Introduction There are two major problems growing in two areas. The first is in Computer Science, in particular software engineering. Software is becoming more and more complex, and hence more susceptible to software defects. Software bugs have two critical repercussions: they cost companies lots of money and time to fix, and they have the potential to cause harm. The National Institute of Standards and Technology estimated that software errors cost the United State's economy approximately sixty billion dollars annually, while the Federal Bureau of Investigations estimated in a 2005 report that software bugs cost U.S. companies approximately sixty-seven billion a year [90, 108]. Software bugs have the potential to cause harm. In 2010 there were a approximately a hundred reports made to the National Highway Traffic Safety Administration of potential problems with the braking system of the 2010 Toyota Prius [17]. The problem was that the anti-lock braking system would experience a \short delay" when the brakes where pressed by the driver of the vehicle [106]. This actually caused some crashes. Toyota found that this short delay was the result of a software bug, and was able to repair the the vehicles using a software update [91]. Another incident where substantial harm was caused was in 2002 where two planes collided over Uberlingen¨ in Germany. A cargo plane operated by DHL collided with a passenger flight holding fifty-one passengers. Air-traffic control did not notice the intersecting traffic until less than a minute before the collision occurred. -
Twelf User's Guide
Twelf User’s Guide Version 1.4 Frank Pfenning and Carsten Schuermann Copyright c 1998, 2000, 2002 Frank Pfenning and Carsten Schuermann Chapter 1: Introduction 1 1 Introduction Twelf is the current version of a succession of implementations of the logical framework LF. Previous systems include Elf (which provided type reconstruction and the operational semantics reimplemented in Twelf) and MLF (which implemented module-level constructs loosely based on the signatures and functors of ML still missing from Twelf). Twelf should be understood as research software. This means comments, suggestions, and bug reports are extremely welcome, but there are no guarantees regarding response times. The same remark applies to these notes which constitute the only documentation on the present Twelf implementation. For current information including download instructions, publications, and mailing list, see the Twelf home page at http://www.cs.cmu.edu/~twelf/. This User’s Guide is pub- lished as Frank Pfenning and Carsten Schuermann Twelf User’s Guide Technical Report CMU-CS-98-173, Department of Computer Science, Carnegie Mellon University, November 1998. Below we state the typographic conventions in this manual. code for Twelf or ML code ‘samp’ for characters and small code fragments metavar for placeholders in code keyboard for input in verbatim examples hkeyi for keystrokes math for mathematical expressions emph for emphasized phrases File names for examples given in this guide are relative to the main directory of the Twelf installation. For example ‘examples/guide/nd.elf’ may be found in ‘/usr/local/twelf/examples/guide/nd.elf’ if Twelf was installed into the ‘/usr/local/’ directory. -
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.