Shallow Binding in LISP 1.5 by Henry G.Baker, Jr
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Introduction to Programming in Lisp
Introduction to Programming in Lisp Supplementary handout for 4th Year AI lectures · D W Murray · Hilary 1991 1 Background There are two widely used languages for AI, viz. Lisp and Prolog. The latter is the language for Logic Programming, but much of the remainder of the work is programmed in Lisp. Lisp is the general language for AI because it allows us to manipulate symbols and ideas in a commonsense manner. Lisp is an acronym for List Processing, a reference to the basic syntax of the language and aim of the language. The earliest list processing language was in fact IPL developed in the mid 1950’s by Simon, Newell and Shaw. Lisp itself was conceived by John McCarthy and students in the late 1950’s for use in the newly-named field of artificial intelligence. It caught on quickly in MIT’s AI Project, was implemented on the IBM 704 and by 1962 to spread through other AI groups. AI is still the largest application area for the language, but the removal of many of the flaws of early versions of the language have resulted in its gaining somewhat wider acceptance. One snag with Lisp is that although it started out as a very pure language based on mathematic logic, practical pressures mean that it has grown. There were many dialects which threaten the unity of the language, but recently there was a concerted effort to develop a more standard Lisp, viz. Common Lisp. Other Lisps you may hear of are FranzLisp, MacLisp, InterLisp, Cambridge Lisp, Le Lisp, ... Some good things about Lisp are: • Lisp is an early example of an interpreted language (though it can be compiled). -
High-Level Language Features Not Found in Ordinary LISP. the GLISP
DOCUMENT RESUME ED 232 860 SE 042 634 AUTHOR Novak, Gordon S., Jr. TITLE GLISP User's Manual. Revised. INSTITUTION Stanford Univ., Calif. Dept. of Computer Science. SPONS AGENCY Advanced Research Projects Agency (DOD), Washington, D.C.; National Science Foundation, Washington, D.C. PUB DATE 23 Nov 82 CONTRACT MDA-903-80-c-007 GRANT SED-7912803 NOTE 43p.; For related documents, see SE 042 630-635. PUB TYPE Guides General (050) Reference Materials General (130) EDRS PRICE MF01/PCO2 Plus Postage. DESCRIPTORS *Computer Programs; *Computer Science; Guides; *Programing; *Programing Languages; *Resource Materials IDENTIFIERS *GLISP Programing Language; National Science Foundation ABSTRACT GLISP is a LISP-based language which provides high-level language features not found in ordinary LISP. The GLISP language is implemented by means of a compiler which accepts GLISP as input and produces ordinary LISP as output. This output can be further compiled to machine code by the LISP compiler. GLISP is available for several ISP dialects, including Interlisp, Maclisp, UCI Lisp, ELISP, Franz Lisp, and Portable Standard Lisp. The goal of GLISP is to allow structured objects to be referenced in a convenient, succinct language and to allow the structures of objects to be changed without changing the code which references the objects. GLISP provides both PASCAL-like and English-like syntaxes; much of the power and brevity of GLISP derive from the compiler features necessary to support the relatively informal, English-like language constructs. Provided in this manual is the documentation necessary for using GLISP. The documentation is presented in the following sections: introduction; object descriptions; reference to objects; GLISP program syntax; messages; context rules and reference; GLISP and knowledge representation languages; obtaining and using GLISP; GLISP hacks (some ways of doing things in GLISP which might not be entirely obvious at first glance); and examples of GLISP object declarations and programs. -
Top-K Entity Resolution with Adaptive Locality-Sensitive Hashing
Top-K Entity Resolution with Adaptive Locality-Sensitive Hashing Vasilis Verroios Hector Garcia-Molina Stanford University Stanford University [email protected] [email protected] ABSTRACT Given a set of records, entity resolution algorithms find all the records referring to each entity. In this paper, we study Entity 1 the problem of top-k entity resolution: finding all the records Entity 2 referring to the k largest (in terms of records) entities. Top-k Top-k Entity entity resolution is driven by many modern applications that Dataset Filtering Entities’ Records Resolution operate over just the few most popular entities in a dataset. We propose a novel approach, based on locality-sensitive hashing, that can very rapidly and accurately process mas- Entity k sive datasets. Our key insight is to adaptively decide how much processing each record requires to ascertain if it refers to a top-k entity or not: the less likely a record is to refer to Figure 1: Filtering stage in the overall workflow. a top-k entity, the less it is processed. The heavily reduced rest. As we will see, this fact makes it easier to find the top- amount of processing for the vast majority of records that k entities without computing all entities. We next illustrate do not refer to top-k entities, leads to significant speedups. two additional applications where one typically only needs Our experiments with images, web articles, and scientific top-k entities. Incidentally, two of the datasets we use for publications show a 2x to 25x speedup compared to the tra- our experiments are from these applications. -
Solving the Funarg Problem with Static Types IFL ’21, September 01–03, 2021, Online
Solving the Funarg Problem with Static Types Caleb Helbling Fırat Aksoy [email protected] fi[email protected] Purdue University Istanbul University West Lafayette, Indiana, USA Istanbul, Turkey ABSTRACT downwards. The upwards funarg problem refers to the manage- The difficulty associated with storing closures in a stack-based en- ment of the stack when a function returns another function (that vironment is known as the funarg problem. The funarg problem may potentially have a non-empty closure), whereas the down- was first identified with the development of Lisp in the 1970s and wards funarg problem refers to the management of the stack when hasn’t received much attention since then. The modern solution a function is passed to another function (that may also have a non- taken by most languages is to allocate closures on the heap, or to empty closure). apply static analysis to determine when closures can be stack allo- Solving the upwards funarg problem is considered to be much cated. This is not a problem for most computing systems as there is more difficult than the downwards funarg problem. The downwards an abundance of memory. However, embedded systems often have funarg problem is easily solved by copying the closure’s lexical limited memory resources where heap allocation may cause mem- scope (captured variables) to the top of the program’s stack. For ory fragmentation. We present a simple extension to the prenex the upwards funarg problem, an issue arises with deallocation. A fragment of System F that allows closures to be stack-allocated. returning function may capture local variables, making the size of We demonstrate a concrete implementation of this system in the the closure dynamic. -
The Evolution of Lisp
1 The Evolution of Lisp Guy L. Steele Jr. Richard P. Gabriel Thinking Machines Corporation Lucid, Inc. 245 First Street 707 Laurel Street Cambridge, Massachusetts 02142 Menlo Park, California 94025 Phone: (617) 234-2860 Phone: (415) 329-8400 FAX: (617) 243-4444 FAX: (415) 329-8480 E-mail: [email protected] E-mail: [email protected] Abstract Lisp is the world’s greatest programming language—or so its proponents think. The structure of Lisp makes it easy to extend the language or even to implement entirely new dialects without starting from scratch. Overall, the evolution of Lisp has been guided more by institutional rivalry, one-upsmanship, and the glee born of technical cleverness that is characteristic of the “hacker culture” than by sober assessments of technical requirements. Nevertheless this process has eventually produced both an industrial- strength programming language, messy but powerful, and a technically pure dialect, small but powerful, that is suitable for use by programming-language theoreticians. We pick up where McCarthy’s paper in the first HOPL conference left off. We trace the development chronologically from the era of the PDP-6, through the heyday of Interlisp and MacLisp, past the ascension and decline of special purpose Lisp machines, to the present era of standardization activities. We then examine the technical evolution of a few representative language features, including both some notable successes and some notable failures, that illuminate design issues that distinguish Lisp from other programming languages. We also discuss the use of Lisp as a laboratory for designing other programming languages. We conclude with some reflections on the forces that have driven the evolution of Lisp. -
Allegro CL User Guide
Allegro CL User Guide Volume 1 (of 2) version 4.3 March, 1996 Copyright and other notices: This is revision 6 of this manual. This manual has Franz Inc. document number D-U-00-000-01-60320-1-6. Copyright 1985-1996 by Franz Inc. All rights reserved. No part of this pub- lication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means electronic, mechanical, by photocopying or recording, or otherwise, without the prior and explicit written permission of Franz incorpo- rated. Restricted rights legend: Use, duplication, and disclosure by the United States Government are subject to Restricted Rights for Commercial Software devel- oped at private expense as specified in DOD FAR 52.227-7013 (c) (1) (ii). Allegro CL and Allegro Composer are registered trademarks of Franz Inc. Allegro Common Windows, Allegro Presto, Allegro Runtime, and Allegro Matrix are trademarks of Franz inc. Unix is a trademark of AT&T. The Allegro CL software as provided may contain material copyright Xerox Corp. and the Open Systems Foundation. All such material is used and distrib- uted with permission. Other, uncopyrighted material originally developed at MIT and at CMU is also included. Appendix B is a reproduction of chapters 5 and 6 of The Art of the Metaobject Protocol by G. Kiczales, J. des Rivieres, and D. Bobrow. All this material is used with permission and we thank the authors and their publishers for letting us reproduce their material. Contents Volume 1 Preface 1 Introduction 1.1 The language 1-1 1.2 History 1-1 1.3 Format -
Java: Exercises in Style
Java: Exercises in Style Marco Faella June 1, 2018 i ii This work is licensed under the Creative Commons Attribution- NonCommercial 4.0 International License. To view a copy of this li- cense, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Author: Marco Faella, [email protected] Preface to the Online Draft This draft is a partial preview of what may eventually become a book. The idea is to share it with the public and obtain feedback to improve its quality. So, if you read the whole draft, or maybe just a chapter, or you just glance over it long enough to spot a typo, let me know! I’m interested in all comments and suggestions for improvement, including (natural) language issues, programming, book structure, etc. I would like to thank all the students who have attended my Programming Languages class, during the past 11 years. They taught me that there is no limit to the number of ways you may think to have solved a Java assignment. Naples, Italy Marco Faella January 2018 iii Contents Contents iv I Preliminaries 1 1 Introduction 3 1.1 Software Qualities . 5 2 Problem Statement 9 2.1 Data Model and Representations . 10 3 Hello World! 13 4 Reference Implementation 17 4.1 Space Complexity . 21 4.2 Time Complexity . 23 II Software Qualities 29 5 Need for Speed 31 5.1 Optimizing “getAmount” and “addWater” . 32 5.2 Optimizing “connectTo” and “addWater” . 34 5.3 The Best Balance: Union-Find Algorithms . -
Graph Reduction Without Pointers
Graph Reduction Without Pointers TR89-045 December, 1989 William Daniel Partain The University of North Carolina at Chapel Hill Department of Computer Science ! I CB#3175, Sitterson Hall Chapel Hill, NC 27599-3175 UNC is an Equal Opportunity/Aflirmative Action Institution. Graph Reduction Without Pointers by William Daniel Partain A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science. Chapel Hill, 1989 Approved by: Jfn F. Prins, reader ~ ~<---( CJ)~ ~ ;=tfJ\ Donald F. Stanat, reader @1989 William D. Partain ALL RIGHTS RESERVED II WILLIAM DANIEL PARTAIN. Graph Reduction Without Pointers (Under the direction of Gyula A. Mag6.) Abstract Graph reduction is one way to overcome the exponential space blow-ups that simple normal-order evaluation of the lambda-calculus is likely to suf fer. The lambda-calculus underlies lazy functional programming languages, which offer hope for improved programmer productivity based on stronger mathematical underpinnings. Because functional languages seem well-suited to highly-parallel machine implementations, graph reduction is often chosen as the basis for these machines' designs. Inherent to graph reduction is a commonly-accessible store holding nodes referenced through "pointers," unique global identifiers; graph operations cannot guarantee that nodes directly connected in the graph will be in nearby store locations. This absence of locality is inimical to parallel computers, which prefer isolated pieces of hardware working on self-contained parts of a program. In this dissertation, I develop an alternate reduction system using "sus pensions" (delayed substitutions), with terms represented as trees and vari ables by their binding indices (de Bruijn numbers). -
A Note on the Optimal Allocation of Spaces in MACLISP by Henry C
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY Al Working Paper 142 March 16, 1977 A Note on the Optimal Allocation of Spaces in MACLISP by Henry C. Baker, Jr. This note describes a method for allocating storage among the various spaces in the MACLISP Implementation of LISP. The optimal strategy which minimizes garbage collector effort allocates free storage among the various spaces in snch a way that they all run out at the same time. In an equilibrium situation, this corresponds to allocating free storage to the spaces in proportion to their usage. Methods are investigated by which the rates of usage can be inferred, and a ge-daemon interrupt handler is developed which implements an approximately optimal strategy in MACLISP. Finally, the sensitivity of this method to rapidly varying differential rates of cell usage is discussed. Key Words and Phrases: garbage collection, list processing, virtual memory, storage management, storage allocation, LISP. CR Categories: 3.50, 3.60, 3.73, 3.80, 4.13, 422, 4.32, 4.33, 4.35, 4.49 This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0522. Working Papers are informal papers intended for internal use. March 16, 1977 A Note on the Optimal Allocation of Spaces in MACLISP Henry C. Baker, Jr. MACLISP [11 unlike some other implementations of. LISP, allocates storage for different types of objects in non-contiguous areas called spaces. -
Special Topics: Programming Languages
Lecture #11 • 0 V22.0490.001 Special Topics: Programming Languages B. Mishra New York University. Lecture # 11 Programming Languages • MISHRA 2008 Lecture #11 • 1 —Slide 1— Common Lisp Language Survey 4 Functional Programming • Pure Functional Programming: Implicit Principle ◦ The value of an expression depends only on the values of its subexpressions, if any. – No side-effect. (No State—No assignment) – An expression has the same value, every time. – Implicit Storage Management: Allocation on Demand + Garbage Collection. – Functions are First Class Objects: 1) As value of an expression 2) As parameters 3) As data Objects. Programming Languages • MISHRA 2008 Lecture #11 • 2 —Slide 2— Common Lisp • LISP: LIst Processing Language Not— Lots of Insidious Sill Parentheses • Second oldest Programming Language (Af- ter Fortran) • Application Areas: 1. Theorem Proving 2. Symbolic Algebra 3. AI (Artificial Intelligence) (Natural Language Processing, Com- puter Vision, Robot Control Systems, Ex- pert Systems, Neural Networks, Automatic Programming) Programming Languages • MISHRA 2008 Lecture #11 • 3 —Slide 3— HISTORY • Developed at MIT AI Lab—1959. LISP 1.5 running on an IBM machine. • BBN LISP (PDP 1/SDS 940) became → INTERLISP (PDP 10) • MACLISP (MIT Project MAC) • LISP 1.6—A version of MACLISP ◦ UCI-LISP (Univ. of Cal. at Irvine) ◦ Standard Lisp (Univ. of Utah) • Lisp Machine Lisp Large Personal Lisp Machine built at MIT • FranzLISP for Vax/UNIX (UC Berkeley) • NIL for Vax/VMS (MIT) • Scheme at MIT • T Lisp at Yale Programming Languages • MISHRA 2008 Lecture #11 • 4 —Slide 4— Common Lisp • 1981/Carnegie-Mellon/Guy L. Steele • Clean Lisp Inconsistencies and illogical conventions were resolved • Transportable Programs written in Common Lisp and debugged in one implementation should run on another ma- chine/implementation without change. -
History of the Lisp Language
History of the Lisp Language History of the Lisp Language The following information is derived from the history section of dpANS Common Lisp. Lisp is a family of languages with a long history. Early key ideas in Lisp were developed by John McCarthy during the 1956 Dartmouth Summer Research Project on Artificial Intelligence. McCarthy’s motivation was to develop an algebraic list processing language for artificial intelligence work. Implementation efforts for early dialects of Lisp were undertaken on the IBM 704, the IBM 7090, the Digital Equipment Corporation (DEC) PDP−1, the DEC PDP−6, and the PDP−10. The primary dialect of Lisp between 1960 and 1965 was Lisp 1.5. By the early 1970’s there were two predominant dialects of Lisp, both arising from these early efforts: MacLisp and Interlisp. For further information about very early Lisp dialects, see The Anatomy of Lisp or Lisp 1.5 Programmer’s Manual. MacLisp improved on the Lisp 1.5 notion of special variables and error handling. MacLisp also introduced the concept of functions that could take a variable number of arguments, macros, arrays, non−local dynamic exits, fast arithmetic, the first good Lisp compiler, and an emphasis on execution speed. For further information about Maclisp, see Maclisp Reference Manual, Revision 0 or The Revised Maclisp Manual. Interlisp introduced many ideas into Lisp programming environments and methodology. One of the Interlisp ideas that influenced Common Lisp was an iteration construct implemented by Warren Teitelman that inspired the loop macro used both on the Lisp Machines and in MacLisp, and now in Common Lisp. -
Tamperproofing a Software Watermark by Encoding Constants
Tamperproofing a Software Watermark by Encoding Constants by Yong He A Thesis Submitted in Partial Fulfilment of the Requirement for the Degree of MASTER OF SCIENCE in Computer Science University of Auckland Copyright © 2002 by Yong He i Abstract Software Watermarking is widely used for software ownership authentication, but it is susceptible to various de-watermarking attacks such as obfuscation. Dynamic Graph Watermarking is a relatively new technology for software watermarking, and is believed the most likely to withstand attacks, which are trying to distort watermark structure. In this thesis, we present a new technology for protecting Dynamic Graph Watermarks. This technology encodes some of the constants, which are found in a software program, into a tree structure that is similar to the watermark, and generates decoding functions to retrieve the value of the constants at program execution time. If the constant tree is modified, the value of some constants will be affected, destroying program correctness. An attacker cannot reliably distinguish the watermark tree from the constant tree, so they must preserve the watermark tree or risk introducing bugs into the program. Constant Encoding technology can be included in Dynamic Graph Watermarking systems as a plug-in module to improve the Dynamic Graph Watermark protection. In this thesis, we present a prototyping program for Constant Encoding technology, which we call the JSafeMark encoder. Besides addressing the issues about Constant Encoding technology, we also discuss the design and implementation of our JSafeMark encoder, and give a practical example to show how this technology can protect Dynamic Graph Watermarking. iii Acknowledgement I would like to thank Professor Clark Thomborson, my supervisor, for his guidance and enthusiasm.