Csc 372 [0.5Cm] Comparative Programming Languages [0.5Cm] 35
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Hal Abelson Education: Princeton AB (Summa Cum Laude)
Hal Abelson Education: Princeton A.B. (summa cum laude) 1969 MIT Ph.D. (Mathematics) 1973 Professional Appointments: 1994–present MIT Class of 1922 Professor MIT 1991–present Full Professor of Computer Sci. and Eng. MIT 1982–1991 Associate Professor of Electrical Eng. and Computer Sci. MIT 1979–1982 Associate Professor, Dept. of EECS and Division for Study and Res. in Education MIT 1977–1979 Assistant Professor, Dept. of EECS and DSRE MIT 1974–1979 Lecturer, Dept. of Mathematics and DSRE MIT 1974–1979 Instructor, Dept. of Mathematics and DSRE MIT Selected publications relevant to this proposal: 1. “Transparent Accountable Data Mining: New Strategies for Privacy Protection,” with T. Berners- Lee, C. Hanson, J, Hendler, L. Kagal, D. McGuinness, G.J. Sussman, K. Waterman, and D. Weitzner. MIT CSAIL Technical Report, 2006-007, January 2006. 2. “Information Accountability,” with Daniel J. Weitzner, Tim Berners-Lee, Joan Feigenbaum, James Hendler, and Gerald Jay Sussman, MIT CSAIL Technical Report, 2007-034, June 2007. Available at http://hdl.handle.net/1721.1/37600. 3. “The Creation of OpenCourseWare at MIT,” J. Science Education and Technology, May, 2007. 4. Structure and Interpretation of Computer Programs, Hal Abelson, Gerald Jay Sussman and Julie Sussman, MIT Press and McGraw-Hill, 1985, (published translations in French, Polish, Chinese, Japanese, Spanish, and German). Second Edition, 1996. 5. “The Risks of Key Recovery, Key Escrow, and Trusted Third-Party Encryption,” with Ross Ander- son, Steven Bellovin, Josh Benaloh, Matt Blaze, Whitfield Diffie, John Gilmore, Peter Neumann, Ronald Rivest, Jeffrey Schiller, and Bruce Schneier, in World Wide Web Journal, vol. 2, no. -
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. -
Stanford University Medical Experimental Computer Resource (SUMEX) Records SC1248
http://oac.cdlib.org/findaid/ark:/13030/c8s46z8g Online items available Guide to the Stanford University Medical Experimental Computer Resource (SUMEX) Records SC1248 Daniel Hartwig & Jenny Johnson Department of Special Collections and University Archives January 2018 Green Library 557 Escondido Mall Stanford 94305-6064 [email protected] URL: http://library.stanford.edu/spc Guide to the Stanford University SC1248 1 Medical Experimental Computer Resource (SUMEX) Records SC... Language of Material: English Contributing Institution: Department of Special Collections and University Archives Title: Stanford University Medical Experimental Computer Resource (SUMEX) records Identifier/Call Number: SC1248 Physical Description: 33 Linear Feet Date (inclusive): 1975-1991 Special Collections and University Archives materials are stored offsite and must be paged 48 hours in advance. For more information on paging collections, see the department's website: http://library.stanford.edu/spc. Conditions Governing Access Materials are open for research use. Audio-visual materials are not available in original format, and must be reformatted to a digital use copy. Conditions Governing Use All requests to reproduce, publish, quote from, or otherwise use collection materials must be submitted in writing to the Head of Special Collections and University Archives, Stanford University Libraries, Stanford, California 94304-6064. Consent is given on behalf of Special Collections as the owner of the physical items and is not intended to include or imply permission from the copyright owner. Such permission must be obtained from the copyright owner, heir(s) or assigns. Restrictions also apply to digital representations of the original materials. Use of digital files is restricted to research and educational purposes. -
Reflexive Interpreters 1 the Problem of Control
Reset reproduction of a Ph.D. thesis proposal submitted June 8, 1978 to the Massachusetts Institute of Technology Electrical Engineering and Computer Science Department. Reset in LaTeX by Jon Doyle, December 1995. c 1978, 1995 Jon Doyle. All rights reserved.. Current address: MIT Laboratory for Computer Science. Available via http://www.medg.lcs.mit.edu/doyle Reflexive Interpreters Jon Doyle [email protected] Massachusetts Institute of Technology, Artificial Intelligence Laboratory Cambridge, Massachusetts 02139, U.S.A. Abstract The goal of achieving powerful problem solving capabilities leads to the “advice taker” form of program and the associated problem of control. This proposal outlines an approach to this problem based on the construction of problem solvers with advanced self-knowledge and introspection capabilities. 1 The Problem of Control Self-reverence, self-knowledge, self-control, These three alone lead life to sovereign power. Alfred, Lord Tennyson, OEnone Know prudent cautious self-control is wisdom’s root. Robert Burns, A Bard’s Epitath The woman that deliberates is lost. Joseph Addison, Cato A major goal of Artificial Intelligence is to construct an “advice taker”,1 a program which can be told new knowledge and advised about how that knowledge may be useful. Many of the approaches towards this goal have proposed constructing additive formalisms for trans- mitting knowledge to the problem solver.2 In spite of considerable work along these lines, formalisms for advising problem solvers about the uses and properties of knowledge are rela- tively undeveloped. As a consequence, the nondeterminism resulting from the uncontrolled application of many independent pieces of knowledge leads to great inefficiencies. -
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. -
Paul Graham's Revenge of the Nerds
Paul Graham’s Revenge of the Nerds What made Lisp different Symbols and S-Expression Trees 6. Programs composed of expressions. Lisp programs are trees of expressions, each of which returns a value. … 7. A symbol type. Symbols are effecBvely pointers to strings stored in a hash table. So you can test equality by comparing a pointer, instead of comparing each character. 8. A notation for code using trees of symbols and constants. CS251 Programming [Lyn adds: these trees are called symbolic expressions = s-expressions] Languages 9. The whole language there all the time. There is no real distinction Spring 2018, Lyn Turbak between read-time, compile-time, and runtime. … reading at runtime enables programs to communicate using s-expressions, an idea recently reinvented Department of Computer Science as XML. [Lyn adds: and JSON!] Wellesley College Symbols & S-expressions 2 Symbols TesBng Symbols for Equality: eq? Lisp was invented to do symbolic processing. (This was thought to be the core of ArBficial Intelligence, and disBnguished Lisp from Fortran (the other The key thing we do with symbols is test them for equality main language at the Bme), whose strength with numerical processing.) with eq? (pronounced “eek”). A symbol is eq? to itself and A key Racket value is the symbol. nothing else. The symbol cat is wriNen (quote cat) or 'cat. > (eq? 'cat 'cat) Symbols are values and so evaluate to themselves. #t > 'cat > (map (λ (s) (eq? s 'to)) 'cat (list 'to 'be 'or 'not 'to 'be)) '(#t #f #f #f #t #f) ; 'thing is just an abbreviation for (quote thing) > (quote cat) 'cat Symbols are similar to strings, except they’re atomic; we don’t do character manipulaBons on them. -
Teach Yourself Scheme in Fixnum Days"
http://xkcd.com/224/ CS 152: Programming Language Paradigms Prof. Tom Austin San José State University What are some programming languages? Taken from http://pypl.github.io/PYPL.html January 2016 Why are there so many? Different domains Different design choices • Flexibility • Type safety • Performance • Build time • Concurrency Which language is better? Good language features • Simplicity • Readability • Learn-ability • Safety • Machine independence • Efficiency These goals almost always conflict Conflict: Type Systems Stop "bad" programs … but ... restrict the programmer Why do we make you take a programming languages course? • You might use one of these languages. • Perhaps one of these languages is the language of the future (whatever that means). • You might see similar languages in your job. • Somebody made us take one, so now we want to make you suffer too. • But most of all… We want to warp your minds. Course goal: change the way that you think about programming. That will make you a better Java programmer. The "Blub" paradox Why do I need higher order functions? My language doesn't have them, and it works just fine!!! "As long as our hypothetical Blub programmer is looking down the power continuum, he knows he's looking down… [Blub programmers are] satisfied with whatever language they happen to use, because it dictates the way they think about programs." --Paul Graham http://www.paulgraham.com/avg.html Languages we will cover (subject to change) Administrative Details • Green sheet: http://www.cs.sjsu.edu/~austin/cs152- spring19/Greensheet.html. • Homework submitted through Canvas: https://sjsu.instructure.com/ • Academic integrity policy: http://info.sjsu.edu/static/catalog/integrity.html Schedule • The class schedule is available through Canvas. -
Implementation Notes
IMPLEMENTATION NOTES XEROX 3102464 lyric Release June 1987 XEROX COMMON LISP IMPLEMENTATION NOTES 3102464 Lyric Release June 1987 The information in this document is subject to change without notice and should not be construed as a commitment by Xerox Corporation. While every effort has been made to ensure the accuracy of this document, Xerox Corporation assumes no responsibility for any errors that may appear. Copyright @ 1987 by Xerox Corporation. Xerox Common Lisp is a trademark. All rights reserved. "Copyright protection claimed includes all forms and matters of copyrightable material and information now allowed by statutory or judicial law or hereinafter granted, including, without limitation, material generated from the software programs which are displayed on the screen, such as icons, screen display looks, etc. " This manual is set in Modern typeface with text written and formatted on Xerox Artificial Intelligence workstations. Xerox laser printers were used to produce text masters. PREFACE The Xerox Common Lisp Implementation Notes cover several aspects of the Lyric release. In these notes you will find: • An explanation of how Xerox Common Lisp extends the Common Lisp standard. For example, in Xerox Common Lisp the Common Lisp array-constructing function make-array has additional keyword arguments that enhance its functionality. • An explanation of how several ambiguities in Steele's Common Lisp: the Language were resolved. • A description of additional features that provide far more than extensions to Common Lisp. How the Implementation Notes are Organized . These notes are intended to accompany the Guy L. Steele book, Common Lisp: the Language which represents the current standard for Co~mon Lisp. -
Fostering Learning in the Networked World: the Cyberlearning Opportunity and Challenge
Inquiries or comments on this report may be directed to the National Science Foundation by email to: [email protected] “Any opinions, findings, conclusions and recommendations expressed in this report are those of the Task Force and do not necessarily reflect or represent the views of the National Science Foundation.” Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge A 21st Century Agenda for the National Science Foundation1 Report of the NSF Task Force on Cyberlearning June 24, 2008 Christine L. Borgman (Chair), Hal Abelson, Lee Dirks, Roberta Johnson, Kenneth R. Koedinger, Marcia C. Linn, Clifford A. Lynch, Diana G. Oblinger, Roy D. Pea, Katie Salen, Marshall S. Smith, Alex Szalay 1 We would like to acknowledge and give special thanks for the continued support and advice from National Science Foundation staff Daniel Atkins, Cora Marrett, Diana Rhoten, Barbara Olds, and Jim Colby. Andrew Lau of the University of California at Los Angeles provided exceptional help and great spirit in making the distributed work of our Task Force possible. Katherine Lawrence encapsulated the Task Force’s work in a carefully crafted Executive Summary. Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge A 21st Century Agenda for the National Science Foundation Science Foundation the National for A 21st Century Agenda Report of the NSF Task Force on Cyberlearning Table of Contents Executive Summary...............................................................................................................................................................................................................................5 -
A Critique of Abelson and Sussman Why Calculating Is Better Than
A critique of Abelson and Sussman - or - Why calculating is better than scheming Philip Wadler Programming Research Group 11 Keble Road Oxford, OX1 3QD Abelson and Sussman is taught [Abelson and Sussman 1985a, b]. Instead of emphasizing a particular programming language, they emphasize standard engineering techniques as they apply to programming. Still, their textbook is intimately tied to the Scheme dialect of Lisp. I believe that the same approach used in their text, if applied to a language such as KRC or Miranda, would result in an even better introduction to programming as an engineering discipline. My belief has strengthened as my experience in teaching with Scheme and with KRC has increased. - This paper contrasts teaching in Scheme to teaching in KRC and Miranda, particularly with reference to Abelson and Sussman's text. Scheme is a "~dly-scoped dialect of Lisp [Steele and Sussman 19781; languages in a similar style are T [Rees and Adams 19821 and Common Lisp [Steele 19821. KRC is a functional language in an equational style [Turner 19811; its successor is Miranda [Turner 1985k languages in a similar style are SASL [Turner 1976, Richards 19841 LML [Augustsson 19841, and Orwell [Wadler 1984bl. (Only readers who know that KRC stands for "Kent Recursive Calculator" will have understood the title of this There are four language features absent in Scheme and present in KRC/Miranda that are important: 1. Pattern-matching. 2. A syntax close to traditional mathematical notation. 3. A static type discipline and user-defined types. 4. Lazy evaluation. KRC and SASL do not have a type discipline, so point 3 applies only to Miranda, Philip Wadhr Why Calculating is Better than Scheming 2 b LML,and Orwell. -
PUB DAM Oct 67 CONTRACT N00014-83-6-0148; N00014-83-K-0655 NOTE 63P
DOCUMENT RESUME ED 290 438 IR 012 986 AUTHOR Cunningham, Robert E.; And Others TITLE Chips: A Tool for Developing Software Interfaces Interactively. INSTITUTION Pittsburgh Univ., Pa. Learning Research and Development Center. SPANS AGENCY Office of Naval Research, Arlington, Va. REPORT NO TR-LEP-4 PUB DAM Oct 67 CONTRACT N00014-83-6-0148; N00014-83-K-0655 NOTE 63p. PUB TYPE Reports - Research/Technical (143) EDRS PRICE MF01/PC03 Plus Postage. DESCRIPTORS *Computer Graphics; *Man Machine Systems; Menu Driven Software; Programing; *Programing Languages IDENTIF7ERS Direct Manipulation Interface' Interface Design Theory; *Learning Research and Development Center; LISP Programing Language; Object Oriented Programing ABSTRACT This report provides a detailed description of Chips, an interactive tool for developing software employing graphical/computer interfaces on Xerox Lisp machines. It is noted that Chips, which is implemented as a collection of customizable classes, provides the programmer with a rich graphical interface for the creation of rich graphical interfaces, and the end-user with classes for modeling the graphical relationships of objects on the screen and maintaining constraints between them. This description of the system is divided into five main sections: () the introduction, which provides background material and a general description of the system; (2) a brief overview of the report; (3) detailed explanations of the major features of Chips;(4) an in-depth discussion of the interactive aspects of the Chips development environment; and (5) an example session using Chips to develop and modify a small portion of an interface. Appended materials include descriptions of four programming techniques that have been sound useful in the development of Chips; descriptions of several systems developed at the Learning Research and Development Centel tsing Chips; and a glossary of key terms used in the report.