DOCSLIB.ORG

Imperial College Computing Student Workshop

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Imperial College Computing Student Workshop Andrew V. Jones (Ed.) Imperial College Computing Student Workshop Proceedings of ICCSW ‘11 September 29th–30th 2011, London UK Volume Editor Andrew V. Jones Verification of Autonomous Systems Group Department of Computing Imperial College London 180 Queen’s Gate, London, SW7 2AZ United Kingdom Email: [email protected] Published a technical report of the Department of Computing, Imperial College London. Technical Report Number: DTR11-9. Cite as: @proceedings{iccsw_2011, editor = {Andrew V. Jones}, title = {Imperial College Computing Student Workshop, 1st Student Workshop, ICCSW ‘11, London, UK, September 29-30, 2011. Proceedings}, booktitle = {ICCSW ‘11}, publisher = {Imperial College London}, series = {Department of Computing Technical Report}, volume = {DTR11-9}, year = {2011} } The copyright of the contents of this volume remains that of the original author(s). Preface This volume contains the proceedings of the inaugural Imperial College Student Workshop (ICCSW ‘11). The workshop took place on 29th–30th September 2011 in London UK. The event was hosted by Imperial College London. ICCSW ‘11 aimed to be a truly student-run workshop: all of the organisation was led by students and the majority of the submitting authors acted as reviewers for other papers. Both the quality and number of the submissions, as well as the high standard of reviews, exceeded the committee’s initial expectations. These proceedings contain 16 original contributions in various fields from across computer science, including both theoretical and applied papers. Both days of the workshop featured a keynote talk on a facet of computer science. The talks were titled: – Artificial Intelligence and Human Thinking, by Robert Kowalski (Imperial College London); and – Building and Operating Products at Google-scale, by Ollie Cook (Google Inc.) The workshop also featured two discussion panels. These panel sessions discussed various areas of computer science from a provocative angle, to allow for stimulating and spontaneous discourse on a given topic. The discussion panels focused on two areas: – The Future of Machine Learning, featuring Aldo Faisal (Imperial College London), Artur Garcez (City University London), Stephen Muggleton (Impe- rial College London), Daniel Roy (University of Cambridge) and Alessandra Russo (Imperial College London); and – Software Engineering in the Age of Multi-Scale Computing, featuring Ollie Cook (Google Inc.), Sam Dutton (Google Inc.), Andrew Eland (Google Inc.) and Juan Silveira (Google Inc.) ICCSW ‘11 proved to be an international event, attracting both submissions and attendees from the UK and the rest of Europe. The workshop was pleased to host over 80 participants from the following countries: Cyprus, France, Greece, Italy, the Netherlands, Romania and the United Kingdom. The organisation of ICCSW ‘11 would not have been possible without the resources and financial support provided by Imperial College London and Google Inc. The latter provided travel bursaries for students and committee members coming from institutions outside of London. Google Inc. also provided one of the keynote talks and members for one of the discussion panels. For this support we are truly grateful! September 2011 Andrew V. Jones London Conference Organisation Steering Committee Xiuyi Fan Department of Computing, Imperial College London Petr Hosek Department of Computing, Imperial College London Andrew V. Jones Department of Computing, Imperial College London Nicholas Ng Department of Computing, Imperial College London Sören Preibusch University of Cambridge Computer Laboratory Reuben Rowe Department of Computing, Imperial College London Malte Schwarzkopf University of Cambridge Computer Laboratory Pia-Ramona Wojtinnek Oxford University Computing Laboratory Advisory Committee Krysia Broda Department of Computing, Imperial College London Naranker Dulay Department of Computing, Imperial College London Sponsors Google Inc. Imperial College London Additional Reviewers Ekaterina Abramova Department of Computing, Imperial College London Konstantinos Barlas National Technical University of Athens Ping-Lin Chang Department of Computing, Imperial College London Leo De Penning TNO Behaviour and Societal Sciences Marco Diciolla University of Oxford Qinquan Gao Department of Computing, Imperial College London Marcel C. Guenther Department of Computing, Imperial College London Evgenios Hadjisoteriou University of Cyprus Bihan Jiang Department of Computing, Imperial College London Spyros Komninos National Technical University of Athens Katerina Ksystra National Technical University of Athens Bjoern Lellmann Department of Computing, Imperial College London Stefan Minica University of Amsterdam Alex Muscar Department of Computer Science, University of Craiova Filipa Peleja Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa v Alan Perotti Università degli Studi di Torino Luke Riley University of Liverpool Pedro Rodrigues Department of Computing, Imperial College London Mark Snaith University of Dundee Nikolaos Triantafyllou National Technical University of Athens Iryna Tsimashenka Department of Computing, Imperial College London Philip Weber University of Birmingham Table of Contents Keynote Talks Building and Operating Products at Google-scale ..................... 1 Ollie Cook Artificial Intelligence and Human Thinking ........................... 2 Robert Kowalski Accepted Papers Combining Markov Decision Processes with Linear Optimal Controllers .. 3 Ekaterina Abramova, Aldo Faisal and Daniel Kuhn Neural-Symbolic Cognitive Agents: Architecture and Theory ........... 10 Leo De Penning, Artur Garcez, Luis Lamb and John-Jules Meyer Time-Bounded Verification of CTMCs against MTL specifications ....... 17 Marco Diciolla MASSPA-Modeller: A Spatial Stochastic Process Algebra modelling tool . 24 Marcel C. Guenther and Jeremy T. Bradley Argumentation and Temporal Persistence ............................ 31 Evgenios Hadjisoteriou and Antonis Kakas Facial Action Recognition using sparse appearance descriptors and their pyramid representations ........................................... 39 Bihan Jiang, Michel Valstar and Maja Pantic A note on a dichotomy for the classes W[P](C) ....................... 46 Björn Lellmann Agent Oriented Programming: from Revolution to Evolution ........... 52 Alex Muscar Conditional Labelling for Abstract Argumentation .................... 59 Alan Perotti, Guido Boella, Dov Gabbay, Leon Van Der Torre and Serena Villata A Persuasive Dialogue Game for Coalition Formation .................. 66 Luke Riley Model-based Self-Adaptive Components: A preliminary approach ....... 73 Pedro Rodrigues and Emil Lupu vii Safe, Flexible Recursive Types for Featherweight Java ................. 80 Reuben Rowe Measuring minimal change in argument premise revision ............... 87 Mark Snaith and Chris Reed Applying Algebraic Specifications on Digital Right Management Systems 94 Nikolaos Triantafyllou, Katerina Ksystra, Petros Stefaneas and Panayi- otis Frangos Reduction of variability in split-merge systems ........................ 101 Iryna Tsimashenka and William Knottenbelt Real-Time Detection of Process Change using Process Mining .......... 108 Philip Weber, Behzad Bordbar and Peter Tino Author Index ................................................... 115 Building and Operating Products at Google-scale Ollie Cook Google Inc. Abstract. Building products for Google-scale presents some unique chal- lenges in the software development and operational spheres. Through exploring techniques relating to design, development, configuration, de- ployment and monitoring the talk will describe how Google delivers high availability products with low latency to millions of customers worldwide. Profile. Ollie Cook is a site reliability manager at Google, managing the oper- ational teams supporting Google Calendar and real-time communications. His teams focus on product and system availability, monitoring, capacity planning and deployment. Prior to joining Google in 2011, Ollie was the technology ser- vices manager at Betfair, an online gambling provider, specialising in low-latency trading systems, and deployment and configuration management of their global computing footprint. Andrew V. Jones (Ed.): Proceedings of the 1st Imperial College Student Workshop (ICCSW ‘11), pp. 1–1. September 29th–30th 2011, London UK. Artificial Intelligence and Human Thinking Robert Kowalski Imperial College London, Department of Computing, Exhibition Road, London SW7 2AZ, UK Abstract. Research in AI has built upon the tools and techniques of many different disciplines, including formal logic, probability theory, de- cision theory, management science, linguistics and philosophy. However, the application of these disciplines in AI has necessitated the development of many enhancements and extensions. Among the most powerful of these are the methods of computational logic. I will argue that computational logic, embedded in an agent cycle, com- bines and improves upon both traditional logic and classical decision theory. I will also argue that many of its methods can be used, not only in AI, but also in ordinary life, to help people improve their own human intelligence without the assistance of computers. Profile. Professor Robert Anthony Kowalski is a Senior Research Investigator and Emeritus Professor of Computational Logic at the Department of Computing of the Imperial College London. Professor Kowalski is recognised for his contributions to logic for knowledge representation and problem solving,
Load more

Recommended publications
  • By Robert Kowalski
    Edinburgh Research Explorer Review of "Computational logic and human thinking: How to be artificially intelligent" by Robert Kowalski Citation for published version: Bundy, A 2012, 'Review of "Computational logic and human thinking: How to be artificially intelligent" by Robert Kowalski', Artificial Intelligence, vol. 191-192, pp. 96-97. https://doi.org/10.1016/j.artint.2012.05.006 Digital Object Identifier (DOI): 10.1016/j.artint.2012.05.006 Link: Link to publication record in Edinburgh Research Explorer Document Version: Early version, also known as pre-print Published In: Artificial Intelligence General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 27. Sep. 2021 Review of \Computational Logic and Human Thinking: How to be Artificially Intelligent" by Robert Kowalski Alan Bundy School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton St, Edinburgh, EH8 9AB, Scotland 1 August 2012 Abstract This is a review of the book \Computational Logic and Human Thinking: How to be Artificially Intelligent" by Robert Kowalski. Keywords: computational logic, human thinking, book review.
    [Show full text]
  • 7. Logic Programming in Prolog
    Artificial Intelligence 7. Logic Programming in Prolog Prof. Bojana Dalbelo Baˇsi´c Assoc. Prof. Jan Snajderˇ University of Zagreb Faculty of Electrical Engineering and Computing Academic Year 2019/2020 Creative Commons Attribution{NonCommercial{NoDerivs 3.0 v1.10 Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 1 / 38 Outline 1 Logic programming and Prolog 2 Inference on Horn clauses 3 Programming in Prolog 4 Non-declarative aspects of Prolog Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 2 / 38 Outline 1 Logic programming and Prolog 2 Inference on Horn clauses 3 Programming in Prolog 4 Non-declarative aspects of Prolog Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 3 / 38 Logic programming Logic programming: use of logic inference as a way of programming Main idea: define the problem in terms of logic formulae, then let the computer do the problem solving (program execution = inference) This is a typical declarative programming approach: express the logic of computation, don't bother with the control flow We focus on the description of the problem (declarative), rather than on how the program is executed (procedural) However, we still need some flow control mechanism, thus: Algorithm = Logic + Control Different from automated theorem proving because: 1 explicit control flow is hard-wired into the program 2 not the full expressivity of FOL is supported Dalbelo Baˇsi´c, Snajderˇ (UNIZG FER) AI { Logic programming AY 2019/2020 4 / 38 Refresher: Declarative programming
    [Show full text]
  • Inconsistency Robustness for Logic Programs Carl Hewitt
    Inconsistency Robustness for Logic Programs Carl Hewitt To cite this version: Carl Hewitt. Inconsistency Robustness for Logic Programs. Inconsistency Robustness, 2015, 978-1- 84890-159. hal-01148496v6 HAL Id: hal-01148496 https://hal.archives-ouvertes.fr/hal-01148496v6 Submitted on 27 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright Inconsistency Robustness for Logic Programs Carl Hewitt This article is dedicated to Alonzo Church and Stanisław Jaśkowski Abstract This article explores the role of Inconsistency Robustness in the history and theory of Logic Programs. Inconsistency Robustness has been a continually recurring issue in Logic Programs from the beginning including Church's system developed in the early 1930s based on partial functions (defined in the lambda calculus) that he thought would allow development of a general logic without the kind of paradoxes that had plagued earlier efforts by Frege, etc.1 Planner [Hewitt 1969, 1971] was a kind of hybrid between the procedural and logical paradigms in that it featured a procedural embedding of logical sentences in that an implication of the form (p implies q) can be procedurally embedded in the following ways: Forward chaining When asserted p, Assert q When asserted q, Assert p Backward chaining When goal q, SetGoal p When goal p, SetGoal q Developments by different research groups in the fall of 1972 gave rise to a controversy over Logic Programs that persists to this day in the form of following alternatives: 1.
    [Show full text]
  • Development of Logic Programming: What Went Wrong, What Was Done About It, and What It Might Mean for the Future
    Development of Logic Programming: What went wrong, What was done about it, and What it might mean for the future Carl Hewitt at alum.mit.edu try carlhewitt Abstract follows: A class of entities called terms is defined and a term is an expression. A sequence of expressions is an Logic Programming can be broadly defined as “using logic expression. These expressions are represented in the to deduce computational steps from existing propositions” machine by list structures [Newell and Simon 1957]. (although this is somewhat controversial). The focus of 2. Certain of these expressions may be regarded as this paper is on the development of this idea. declarative sentences in a certain logical system which Consequently, it does not treat any other associated topics will be analogous to a universal Post canonical system. related to Logic Programming such as constraints, The particular system chosen will depend on abduction, etc. programming considerations but will probably have a The idea has a long development that went through single rule of inference which will combine substitution many twists in which important questions turned out to for variables with modus ponens. The purpose of the combination is to avoid choking the machine with special have surprising answers including the following: cases of general propositions already deduced. Is computation reducible to logic? 3. There is an immediate deduction routine which when Are the laws of thought consistent? given a set of premises will deduce a set of immediate This paper describes what went wrong at various points, conclusions. Initially, the immediate deduction routine what was done about it, and what it might mean for the will simply write down all one-step consequences of the future of Logic Programming.
    [Show full text]
  • Logic for Problem Solving
    LOGIC FOR PROBLEM SOLVING Robert Kowalski Imperial College London 12 October 2014 PREFACE It has been fascinating to reflect and comment on the way the topics addressed in this book have developed over the past 40 years or so, since the first version of the book appeared as lecture notes in 1974. Many of the developments have had an impact, not only in computing, but also more widely in such fields as math- ematical, philosophical and informal logic. But just as interesting (and perhaps more important) some of these developments have taken different directions from the ones that I anticipated at the time. I have organised my comments, chapter by chapter, at the end of the book, leaving the 1979 text intact. This should make it easier to compare the state of the subject in 1979 with the subsequent developments that I refer to in my commentary. Although the main purpose of the commentary is to look back over the past 40 years or so, I hope that these reflections will also help to point the way to future directions of work. In particular, I remain confident that the logic-based approach to knowledge representation and problem solving that is the topic of this book will continue to contribute to the improvement of both computer and human intelligence for a considerable time into the future. SUMMARY When I was writing this book, I was clear about the problem-solving interpretation of Horn clauses, and I knew that Horn clauses needed to be extended. However, I did not know what extensions would be most important.
    [Show full text]
  • Copyright by Amelia J. Harrison 2017
    Copyright by Amelia J. Harrison 2017 The Dissertation Committee for Amelia J. Harrison certifies that this is the approved version of the following dissertation: Formal Methods for Answer Set Programming Committee: Vladimir Lifschitz, Supervisor Robert S. Boyer Isil Dillig Warren A. Hunt, Jr. Torsten Schaub Formal Methods for Answer Set Programming by Amelia J. Harrison Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin December 2017 To Grandmother Betsy Acknowledgments It’s not an exaggeration to say that were it not for many people other than myself this document would not exist. This is by no means an exhaustive list. I’ve had the opportunity to collaborate with many wonderful and talented colleagues. Among them are Daniel Bailey, Martin Gebser, Yuliya Lierler, Roland Kaminski, Julian Michael, David Pearce, Dhananjay Raju, Mirek Truszczynski, Agustin Valverde, and Fangkai Yang. Without friends to help maintain balance, it’s entirely unclear if I would have been able to see this through. Backyard barbecues and Game of Thrones viewing parties have been essential. I feel especially lucky to have met early on, and shared much of this journey with Hannah Alpert-Abrams and Kate Mesh. My family has been a constant source of reassurance and has helped me nav- igate many of the inevitable bouts of self-doubt that arise in the process of a Ph.D. This includes my parents, Jack and Betsy Harrison, who have always unequivocally encouraged me to find my own path.
    [Show full text]
  • CSCI 334: Principles of Programming Languages Lecture 21: Logic
    Announcements CSCI 334: Principles of Programming Languages Be a TA! Lecture 21: Logic Programming Instructor: Dan Barowy Announcements Logic Programming • Logic programming began as a collaboration between AI researchers (e.g., John McCarthy) and logicians (e.g., John Alan Robinson) to solve problems in artificial intelligence. • Cordell Green built the first “question and answer” system using Robinson’s “unification algorithm,” demonstrating that it was practical to prove theorems automatically. Prolog Declarative Programming • Alain Colmerauer and Phillippe Roussel at Aix- Marseille University invented Prolog in 1972. • Declarative programming is a very different style of programming than • They were inspired by a particular formulation of you have seen to this point. logic, called “Horn clauses,” popularized by the • Mostly, you have seen imperative programs. logician Robert Kowalski. • In imperative-style programming, the programmer instructs the • Horn clauses have a “procedural interpretation,” computer how to compute the desired result. meaning that they suggest a simple procedure for • In declarative-style programming, the computer already knows how solving them, called “resolution.” to compute results. • John Alan Robinson’s unification algorithm is an • Instead, the programmer asks the computer what to compute. efficient algorithm for doing resolution, and this is essentially the algorithm used by Prolog. Declarative Programming Prolog • The goal of AI is to enable a computer to answer declarative queries. • Most of you have probably been CS majors for long enough that we • I.e., it already knows how to answer you. have sufficiently damaged your brain such that you do not recognize • Prolog was an attempt to solve this problem. the difference between these two concepts.
    [Show full text]
  • Database Updates in the Event Calculus
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector J. LOGIC PROGRAMMING 1992:12:121-146 121 DATABASE UPDATES IN THE EVENT CALCULUS ROBERT KOWALSKI D This paper investigates a special case of the event calculus, concerned with database updates. It discusses the way relational databases, historical databases, modal logic, the situation calculus, and case semantics deal with database updates and compares the event calculus with the situation calculus in detail. It argues that the event calculus can overcome the computational aspects of the frame problem in the situation calculus and that it can be implemented with an efficiency approaching that of destruc- tive assignment in relational databases. a INTRODUCTION The event calculus [91 was developed as a theory for reasoning about events in a logic-programming framework. It is based in part on the situation calculus [13,14], but focuses on the concept of event as highlighted in semantic network representa- tions of case semantics. Its main intended application is the representation of events in database updates and discourse representation. It is closely related to Allen’s interval temporal logic [l, 21 and Lee-Coelho-Cotta’s treatment of time in deductive databases [ll]. The relationship between the event calculus and the systems of Allen and Lee-Coelho-Cotta has been investigated by Sadri [15]. The event calculus is concerned with formalizing the effect of events on objects, their properties, and their relationships. Thus, for example, given a description of an event in which Bob gives Book1 to John, Address correspondence to Robert Kowalski, Department of Computing, Imperial College of Science and Technology, 180 Queen’s Gate, London SW7 2BZ, England.
    [Show full text]
  • History of Logic Programming
    LOGIC PROGRAMMING Robert Kowalski 1 INTRODUCTION The driving force behind logic programming is the idea that a single formalism suffices for both logic and computation, and that logic subsumes computation. But logic, as this series of volumes proves, is a broad church, with many denomi- nations and communities, coexisting in varying degrees of harmony. Computing is, similarly, made up of many competing approaches and divided into largely disjoint areas, such as programming, databases, and artificial intelligence. On the surface, it might seem that both logic and computing suffer from a similar lack of cohesion. But logic is in better shape, with well-understood relationships between different formalisms. For example, first-order logic extends propositional logic, higher-order logic extends first-order logic, and modal logic extends classical logic. In contrast, in Computing, there is hardly any relationship between, for example, Turing machines as a model of computation and relational algebra as a model of database queries. Logic programming aims to remedy this deficiency and to unify different areas of computing by exploiting the greater generality of logic. It does so by building upon and extending one of the simplest, yet most powerful logics imaginable, namely the logic of Horn clauses. In this paper, which extends a shorter history of logic programming (LP) in the 1970s [Kowalski, 2013], I present a personal view of the history of LP, focusing on logical, rather than on technological issues. I assume that the reader has some background in logic, but not necessarily in LP. As a consequence, this paper might also serve a secondary function, as a survey of some of the main developments in the logic of LP.
    [Show full text]
  • Published in Arxiv
    Published in ArXiv http://arxiv.org/abs/0904.3036 Middle History of Logic Programming Resolution, Planner, Edinburg LCF, Prolog, Simula, and the Japanese Fifth Generation Project Carl Hewitt ©2013 http://carlhewitt.info This paper is dedicated to Alonzo Church and Stanislaw Jaśkowski. Logic Programming can be broadly defined as “using logic to infer computational steps from existing propositions” However, mathematical logic cannot always infer computational steps because computational systems make use of arbitration for determining which message is processed next by a recipient that is sent multiple messages concurrently. Since arrival orders are in general indeterminate, they cannot be inferred from prior information by mathematical logic alone. Therefore mathematical logic cannot in general implement computation. This conclusion is contrary to Robert Kowalski who stated “Looking back on our early discoveries, I value most the discovery that computation could be subsumed by deduction.” Nevertheless, logic programming (like functional programming) can be a useful programming idiom. Over the course of history, the term “functional programming” has grown more precise and technical as the field has matured. Logic Programming should be on a similar trajectory. Accordingly, “Logic Programming” should have a general precise characterization. Kowalski's approach has been to advocate limiting Logic Programming to backward-chaining only inference based on resolution. In contrast, our approach is explore Logic Programming using inconsistency-robust Natural Deduction that is contrary to requiring (usually undesirable) reduction to conjunctive normal form in order to use resolution. Because contemporary large software systems are pervasively inconsistent, it is not safe to reason about them using classical logic, e.g., using resolution theorem proving.
    [Show full text]
  • Logic Programming Robert Kowalski MIT Encyclopaedia of Cognitive
    Logic Programming Robert Kowalski MIT Encyclopaedia of Cognitive Science (eds. R A Wilson and F C Keil) MIT Press, 1999, pp. 484-486. Logic programming is the use of logic to represent programs and of deduction to execute programs in logical form. To this end, many different forms of logic and many varieties of deduction have been investigated. The simplest form of logic, which is the basis of most logic programming, is the Horn clause subset of logic, where programs consist of sets of implications: A0 if A1 and A2 and ... and An. Here each Ai is a simple (i.e. atomic) sentence. It is usual to write such implications backward. This is because deduction by backward reasoning interprets them as procedures: to solve the goal A0, solve the subgoals A1 and A2 and ... and An. The number of conditions, n, can be 0, in which case the implication is simply a fact, which behaves as a procedure which solves goals directly without introducing subgoals. The procedural interpretation of implications can be used for declarative programming, where the programmer describes information in logical form and the deduction mechanism uses backward reasoning to achieve problem solving behavior. Consider, for example, the implication X is a citizen of the USA if X was born in the USA. Backward reasoning treats the sentence as a procedure To show X is a citizen of the USA, show that X was born in the USA. In fact, backward reasoning can be used, not only to show a particular person is a citizen, but it can also be used to find people who are citizens by finding people who were born in the USA.
    [Show full text]
  • The Birth of Prolog
    The birth of Prolog Alain Colmerauer and Philippe Roussel November 1992 Abstract The programming language, Prolog, was born of a project aimed not at producing a programming language but at processing natural languages; in this case, French. The project gave rise to a preliminary version of Prolog at the end of 1971 and a more definitive version at the end of 1972. This article gives the history of this project and describes in detail the preliminary and then the final versions of Prolog. The authors also felt it appropriate to describe the Q-systems since it was a language which played a prominent part in Prolog’s genesis. Contents 1 Introduction 2 2 Part I. The history 3 2.1 1971: The first steps . 3 2.2 1972: The application that created Prolog . 6 2.3 1973: The final Prolog . 9 2.4 1974 and 1975: The distribution of Prolog . 10 3 Part II. A forerunner of Prolog, the Q-systems 12 3.1 One-way unification . 12 3.2 Rule application strategy . 13 3.3 Implementation . 15 4 Part III. The preliminary Prolog 15 4.1 Reasons for the choice of resolution method . 16 4.2 Characteristics of the preliminary Prolog . 18 4.3 Implementation of the preliminary Prolog . 20 5 Part IV. The final Prolog 21 5.1 Resolution strategy . 22 5.2 Syntax and Primitives . 23 5.3 A programming example . 24 5.4 Implementation of the interpreter . 28 1 6 Conclusion 29 1 Introduction As is well known, the name ‘Prolog’ was invented in Marseilles in 1972.
    [Show full text]