An Overview of Common Lisp
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GNU/Linux AI & Alife HOWTO
GNU/Linux AI & Alife HOWTO GNU/Linux AI & Alife HOWTO Table of Contents GNU/Linux AI & Alife HOWTO......................................................................................................................1 by John Eikenberry..................................................................................................................................1 1. Introduction..........................................................................................................................................1 2. Symbolic Systems (GOFAI)................................................................................................................1 3. Connectionism.....................................................................................................................................1 4. Evolutionary Computing......................................................................................................................1 5. Alife & Complex Systems...................................................................................................................1 6. Agents & Robotics...............................................................................................................................1 7. Statistical & Machine Learning...........................................................................................................2 8. Missing & Dead...................................................................................................................................2 1. Introduction.........................................................................................................................................2 -
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. -
The Copyright Law of the United States (Title 17, U.S
NOTICE WARNING CONCERNING COPYRIGHT RESTRICTIONS: The copyright law of the United States (title 17, U.S. Code) governs the making of photocopies or other reproductions of copyrighted material. Any copying of this document without permission of its author may be prohibited by law. CMU Common Lisp User's Manual Mach/IBM RT PC Edition David B. McDonald, Editor April 1989 CMU-CS-89-132 . School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 This is a revised version of Technical Report CMU-CS-87-156. Companion to Common Lisp: The Language Abstract CMU Common Lisp is an implementation of Common Lisp that currently runs on the IBM RT PC under Mach, a Berkeley Unix 4.3 binary compatible operating system. This document describes the implementation dependent choices made in developing this implementation of Common Lisp. Also, several extensions have been added, including the proposed error system, a stack crawling debugger, a stepper, an interface to Mach system calls, a foreign function call interface, the ability to write assembler language routines, and other features that provide a good environment for developing Lisp code. This research was sponsored by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976 under contract F33615-87-C-1499 and monitored by the Avionics Laboratory, Air Force Wright Aeronautical Laboratories, Aeronautical Systems Division (AFSC), Wright-Patterson AFB, OHIO 45433-6543. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. -
An Implementation of Python for Racket
An Implementation of Python for Racket Pedro Palma Ramos António Menezes Leitão INESC-ID, Instituto Superior Técnico, INESC-ID, Instituto Superior Técnico, Universidade de Lisboa Universidade de Lisboa Rua Alves Redol 9 Rua Alves Redol 9 Lisboa, Portugal Lisboa, Portugal [email protected] [email protected] ABSTRACT Keywords Racket is a descendent of Scheme that is widely used as a Python; Racket; Language implementations; Compilers first language for teaching computer science. To this end, Racket provides DrRacket, a simple but pedagogic IDE. On the other hand, Python is becoming increasingly popular 1. INTRODUCTION in a variety of areas, most notably among novice program- The Racket programming language is a descendent of Scheme, mers. This paper presents an implementation of Python a language that is well-known for its use in introductory for Racket which allows programmers to use DrRacket with programming courses. Racket comes with DrRacket, a ped- Python code, as well as adding Python support for other Dr- agogic IDE [2], used in many schools around the world, as Racket based tools. Our implementation also allows Racket it provides a simple and straightforward interface aimed at programs to take advantage of Python libraries, thus signif- inexperienced programmers. Racket provides different lan- icantly enlarging the number of usable libraries in Racket. guage levels, each one supporting more advanced features, that are used in different phases of the courses, allowing Our proposed solution involves compiling Python code into students to benefit from a smoother learning curve. Fur- semantically equivalent Racket source code. For the run- thermore, Racket and DrRacket support the development of time implementation, we present two different strategies: additional programming languages [13]. -
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. -
Bringing GNU Emacs to Native Code
Bringing GNU Emacs to Native Code Andrea Corallo Luca Nassi Nicola Manca [email protected] [email protected] [email protected] CNR-SPIN Genoa, Italy ABSTRACT such a long-standing project. Although this makes it didactic, some Emacs Lisp (Elisp) is the Lisp dialect used by the Emacs text editor limitations prevent the current implementation of Emacs Lisp to family. GNU Emacs can currently execute Elisp code either inter- be appealing for broader use. In this context, performance issues preted or byte-interpreted after it has been compiled to byte-code. represent the main bottleneck, which can be broken down in three In this work we discuss the implementation of an optimizing com- main sub-problems: piler approach for Elisp targeting native code. The native compiler • lack of true multi-threading support, employs the byte-compiler’s internal representation as input and • garbage collection speed, exploits libgccjit to achieve code generation using the GNU Com- • code execution speed. piler Collection (GCC) infrastructure. Generated executables are From now on we will focus on the last of these issues, which con- stored as binary files and can be loaded and unloaded dynamically. stitutes the topic of this work. Most of the functionality of the compiler is written in Elisp itself, The current implementation traditionally approaches the prob- including several optimization passes, paired with a C back-end lem of code execution speed in two ways: to interface with the GNU Emacs core and libgccjit. Though still a work in progress, our implementation is able to bootstrap a func- • Implementing a large number of performance-sensitive prim- tional Emacs and compile all lexically scoped Elisp files, including itive functions (also known as subr) in C. -
Common Lispworks User Guide
LispWorks® for the Windows® Operating System Common LispWorks User Guide Version 5.1 Copyright and Trademarks Common LispWorks User Guide (Windows version) Version 5.1 February 2008 Copyright © 2008 by LispWorks Ltd. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of LispWorks Ltd. The information in this publication is provided for information only, is subject to change without notice, and should not be construed as a commitment by LispWorks Ltd. LispWorks Ltd assumes no responsibility or liability for any errors or inaccuracies that may appear in this publication. The software described in this book is furnished under license and may only be used or copied in accordance with the terms of that license. LispWorks and KnowledgeWorks are registered trademarks of LispWorks Ltd. Adobe and PostScript are registered trademarks of Adobe Systems Incorporated. Other brand or product names are the registered trade- marks or trademarks of their respective holders. The code for walker.lisp and compute-combination-points is excerpted with permission from PCL, Copyright © 1985, 1986, 1987, 1988 Xerox Corporation. The XP Pretty Printer bears the following copyright notice, which applies to the parts of LispWorks derived therefrom: Copyright © 1989 by the Massachusetts Institute of Technology, Cambridge, Massachusetts. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, pro- vided that this copyright and permission notice appear in all copies and supporting documentation, and that the name of M.I.T. -
Omnipresent and Low-Overhead Application Debugging
Omnipresent and low-overhead application debugging Robert Strandh [email protected] LaBRI, University of Bordeaux Talence, France ABSTRACT application programmers as opposed to system programmers. The state of the art in application debugging in free Common The difference, in the context of this paper, is that the tech- Lisp implementations leaves much to be desired. In many niques that we suggest are not adapted to debugging the cases, only a backtrace inspector is provided, allowing the system itself, such as the compiler. Instead, throughout this application programmer to examine the control stack when paper, we assume that, as far as the application programmer an unhandled error is signaled. Most such implementations do is concerned, the semantics of the code generated by the not allow the programmer to set breakpoints (unconditional compiler corresponds to that of the source code. or conditional), nor to step the program after it has stopped. In this paper, we are mainly concerned with Common Furthermore, even debugging tools such as tracing or man- Lisp [1] implementations distributed as so-called FLOSS, i.e., ually calling break are typically very limited in that they do \Free, Libre, and Open Source Software". While some such not allow the programmer to trace or break in important sys- implementations are excellent in terms of the quality of the tem functions such as make-instance or shared-initialize, code that the compiler generates, most leave much to be simply because these tools impact all callers, including those desired when it comes to debugging tools available to the of the system itself, such as the compiler. -
How Lisp Systems Look Different in Proceedings of European Conference on Software Maintenance and Reengineering (CSMR 2008)
How Lisp Systems Look Different In Proceedings of European Conference on Software Maintenance and Reengineering (CSMR 2008) Adrian Dozsa Tudor Gˆırba Radu Marinescu Politehnica University of Timis¸oara University of Berne Politehnica University of Timis¸oara Romania Switzerland Romania [email protected] [email protected] [email protected] Abstract rently used in a variety of domains, like bio-informatics (BioBike), data mining (PEPITe), knowledge-based en- Many reverse engineering approaches have been devel- gineering (Cycorp or Genworks), video games (Naughty oped to analyze software systems written in different lan- Dog), flight scheduling (ITA Software), natural language guages like C/C++ or Java. These approaches typically processing (SRI International), CAD (ICAD or OneSpace), rely on a meta-model, that is either specific for the language financial applications (American Express), web program- at hand or language independent (e.g. UML). However, one ming (Yahoo! Store or reddit.com), telecom (AT&T, British language that was hardly addressed is Lisp. While at first Telecom Labs or France Telecom R&D), electronic design sight it can be accommodated by current language inde- automation (AMD or American Microsystems) or planning pendent meta-models, Lisp has some unique features (e.g. systems (NASA’s Mars Pathfinder spacecraft mission) [16]. macros, CLOS entities) that are crucial for reverse engi- neering Lisp systems. In this paper we propose a suite of Why Lisp is Different. In spite of its almost fifty-year new visualizations that reveal the special traits of the Lisp history, and of the fact that other programming languages language and thus help in understanding complex Lisp sys- borrowed concepts from it, Lisp still presents some unique tems. -
An Evaluation of Go and Clojure
An evaluation of Go and Clojure A thesis submitted in partial satisfaction of the requirements for the degree Bachelors of Science in Computer Science Fall 2010 Robert Stimpfling Department of Computer Science University of Colorado, Boulder Advisor: Kenneth M. Anderson, PhD Department of Computer Science University of Colorado, Boulder 1. Introduction Concurrent programming languages are not new, but they have been getting a lot of attention more recently due to their potential with multiple processors. Processors have gone from growing exponentially in terms of speed, to growing in terms of quantity. This means processes that are completely serial in execution will soon be seeing a plateau in performance gains since they can only rely on one processor. A popular approach to using these extra processors is to make programs multi-threaded. The threads can execute in parallel and use shared memory to speed up execution times. These multithreaded processes can significantly speed up performance, as long as the number of dependencies remains low. Amdahl‘s law states that these performance gains can only be relative to the amount of processing that can be parallelized [1]. However, the performance gains are significant enough to be looked into. These gains not only come from the processing being divvied up into sections that run in parallel, but from the inherent gains from sharing memory and data structures. Passing new threads a copy of a data structure can be demanding on the processor because it requires the processor to delve into memory and make an exact copy in a new location in memory. Indeed some studies have shown that the problem with optimizing concurrent threads is not in utilizing the processors optimally, but in the need for technical improvements in memory performance [2]. -
Wiki Comunità Di Pratica
STRUMENTI SOFTWARE PER LA COOPERAZIONE DI RETE Comunità Wiki Comunità di pratica Le comunità di pratica e di apprendimento sono gruppi sociali che hanno come obiettivo finale il generare conoscenza organizzata e di qualità cui ogni individuo può avere libero accesso. In queste comunità gli individui mirano a un apprendimento continuo e hanno consapevolezza delle proprie conoscenze. Non esistono differenze di tipo gerarchico: tutti hanno uguale importanza perché il lavoro di ciascuno è beneficio per l’intera comunità. La finalità è il miglioramento collettivo. Chi entra in questo tipo di organizzazione mira a un modello di condivisione; non esistono spazi privati o individuali, in quanto tutti condividono tutto. Chi ha conoscenza e la tiene per sé è come se non l’avesse. Le comunità di pratica tendono all'eccellenza, a prendere ciò che di meglio produce ognuno dei collaboratori. Questo metodo costruttivista punta ad una conoscenza che si costruisce insieme e rappresenta un modo di vivere, lavorare e studiare. É questa una concezione che si differenzia notevolmente dalle società di tipo individualistico. Tra queste troviamo la società occidentale dove tra gli uomini prevale la competizione e manca quella collaborazione che invece funge da motore pulsante nelle comunità di pratica. Le teorie di McLuhan Fra i più importanti teorici delle comunità di pratica c'è Marshall McLuhan. Negli strumenti del comunicare egli afferma: "nel regime della tecnologia elettrica il compito dell’uomo diventa quello di imparare e di sapere; tutte le forme di ricchezza derivano dallo spostamento d’informazione". Secondo il mito greco dell'alfabeto, prima dell'arrivo di re Cadmo (che introdusse in Grecia le lettere fonetiche), la conoscenza e il potere erano monopolio sacerdotale, in quanto la scrittura prealfabetica, con i suoi innumerevoli segni, era difficile da apprendere.