Inconsistency Robustness for Logic Programs Carl Hewitt
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Panel: Large Knowledge Bases
From: AAAI Technical Report SS-02-06. Compilation copyright © 2002, AAAI (www.aaai.org). All rights reserved. Panel: Large KnowledgeBases AdamPease (Teknowledge, chair), Chris Welty (Vassar College), Pat Hayes (U. West Florida), Anthony G. Cohn (U. Leeds), Ken Murray (SRI) Fellbaum, C. (1998). WordNet, An Electronic Lexical Database. MITPress. Abstract Lenat, D., 1995, "Cyc: A Large-Scale Investment in It is estimated that 1-2 exabytes of data is now being KnowledgeInfrastructure". Communicationsof the ACM generated each year, almost all of it in purely digital form 38, no. 1 !, November.See also http://www.c¥c.com (Lymanet. ai. 2000). Properly structured, this information could form a global knowledge base. Currently however, Lyman,P., Varian, H., Dunn, J., Strygin, A., Swearingen, this information exists in manydifferent forms, manyof K., (2000). HowMuch Information?, University California, which are only suitable for humanconsumption, and which Berkeley are largely opaque to computerbased understanding. Majorefforts to build large formal ontologies or address http://www.sims.berkeley.edu/research/project.,ghow-much- issues in their construction have been undertaken funded by info the government in the US such as the DARPAKnowledge Niles, I., & Pease, A., (2001), Towarda Standard Upper Sharing Effort (Patil et al, 1992), High Performance Ontology, in Proceedings of the 2nd International KnowledgeBases (Cohen et. al., 1998), Rapid Knowledge Conference on Formal Ontology in Information Systems Formation (RKF, 2002) and in Europe including Advanced (FOIS-2001). See also http:llonlology.teknowledge.com KnowledgeTechnologies (Shadboit, 2001) and OntoWeb and http:l/suo.ieee.org (OntoWeb,2002), as international standards efforts such the IEEE Standard Upper Ontology (Niles & Pease, 2001) OntoWeb(2002). -
A Critique of Pure Reason’
151 A critique of pure reason’ DREWMCDERMOTT Yale University, New Haven, CT 06S20, U.S.A. Cornput. Intell. 3. 151-160 (1987) In 1978, Patrick Hayes promulgated the Naive Physics Man- the knowledge that programs must have before we write the ifesto. (It finally appeared as an “official” publication in programs themselves. We know what this knowledge is; it’s Hobbs and Moore 1985.) In this paper, he proposed that an all- what everybody knows, about physics, about time and space, out effort be mounted to formalize commonsense knowledge, about human relationships and behavior. If we attempt to write using first-order logic as a notation. This effort had its roots in the programs first, experience shows that the knowledge will earlier research, especially the work of John McCarthy, but the be shortchanged. The tendency will be to oversimplify what scope of Hayes’s proposal was new and ambitious. He sug- people actually know in order to get a program that works. On gested that the use of Tarskian seniantics could allow us to the other hand, if we free ourselves from the exigencies of study a large volume of knowledge-representation problems hacking, then we can focus on the actual knowledge in all its free from the confines of computer programs. The suggestion complexity. Once we have a rich theory of the commonsense inspired a small community of people to actually try to write world, we can try to embody it in programs. This theory will down all (or most) of commonsense knowledge in predictate become an indispensable aid to writing those programs. -
Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin, Speech and Language Processing
ENTRY ARTIFICIAL INTELLIGENCE [ENTRY ARTIFICIAL INTELLIGENCE] Authors: Oliver Knill: March 2000 Literature: Peter Norvig, Paradigns of Artificial Intelligence Programming Daniel Juravsky and James Martin, Speech and Language Processing Adaptive Simulated Annealing [Adaptive Simulated Annealing] A language interface to a neural net simulator. artificial intelligence [artificial intelligence] (AI) is a field of computer science concerned with the concepts and methods of symbolic knowledge representation. AI attempts to model aspects of human thought on computers. Aspectrs of AI: computer vision • language processing • pattern recognition • expert systems • problem solving • roboting • optical character recognition • artificial life • grammars • game theory • Babelfish [Babelfish] Online translation system from Systran. Chomsky [Chomsky] Noam Chomsky is a pioneer in formal language theory. He is MIT Professor of Linguistics, Linguistic Theory, Syntax, Semantics and Philosophy of Language. Eliza [Eliza] One of the first programs to feature English output as well as input. It was developed by Joseph Weizenbaum at MIT. The paper appears in the January 1966 issue of the "Communications of the Association of Computing Machinery". Google [Google] A search engine emerging at the end of the 20'th century. It has AI features, allows not only to answer questions by pointing to relevant webpages but can also do simple tasks like doing arithmetic computations, convert units, read the news or find pictures with some content. GPS [GPS] General Problem Solver. A program developed in 1957 by Alan Newell and Herbert Simon. The aim was to write a single computer program which could solve any problem. One reason why GPS was destined to fail is now at the core of computer science. -
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. -
Conceptualization and Visualization of Tagging and Folksonomies
Conceptualization and Visualization of Tagging and Folksonomies Von der Fakultät für Ingenieurwissenschaften, Abteilung Informatik und Angewandte Kognitionswissenschaft der Universität Duisburg-Essen zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften (Dr.-Ing.) genehmigte Dissertation von Steffen Lohmann aus Hamburg 1. Gutachter: Prof. Dr. Maria Paloma Díaz Pérez 2. Gutachter: Prof. Dr.-Ing. Jürgen Ziegler Tag der mündlichen Prüfung: 27.11.2013 Hinweis: Diese Dissertation ist im Rahmen eines binationalen Promotionsverfahrens (Cotutelle) in Kooperation mit der Universidad Carlos III de Madrid entstanden. Abstract Tagging has become a popular indexing method for interactive systems in the past decade. It offers a simple yet effective way for users to organize an ever increasing amount of digital information for themselves and/or others. The linked user vocabulary resulting from tagging is known as folksonomy and provides a valuable source for the retrieval and exploration of digital resources. Although several models and representations of tagging have been proposed, there is no coherent conceptualization that provides a comprehensive and pre- cise description of the concepts and relationships in the domain. Furthermore, there is little systematic research in the area of folksonomy visualization, and so folksonomies are still mainly depicted as simple tag clouds. Both problems are related, as a well-defined conceptualization is an important prerequisite for the interoperable use and visualization of folksonomies. The thesis addresses these shortcomings by developing a coherent conceptualiza- tion of tagging and visualizations for the interactive exploration of folksonomies. It gives an overview and comparison of tagging models and defines key concepts of the domain. After a comprehensive review of existing tagging ontologies, a unified and coherent conceptualization is presented that incorporates the best parts of the reviewed ontologies. -
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 -
UC Berkeley Previously Published Works
UC Berkeley UC Berkeley Previously Published Works Title Building the Second Mind, 1961-1980: From the Ascendancy of ARPA-IPTO to the Advent of Commercial Expert Systems Permalink https://escholarship.org/uc/item/7ck3q4f0 ISBN 978-0-989453-4-6 Author Skinner, Rebecca Elizabeth Publication Date 2013-12-31 eScholarship.org Powered by the California Digital Library University of California Building the Second Mind, 1961-1980: From the Ascendancy of ARPA to the Advent of Commercial Expert Systems copyright 2013 Rebecca E. Skinner ISBN 978 09894543-4-6 Forward Part I. Introduction Preface Chapter 1. Introduction: The Status Quo of AI in 1961 Part II. Twin Bolts of Lightning Chapter 2. The Integrated Circuit Chapter 3. The Advanced Research Projects Agency and the Foundation of the IPTO Chapter 4. Hardware, Systems and Applications in the 1960s Part II. The Belle Epoque of the 1960s Chapter 5. MIT: Work in AI in the Early and Mid-1960s Chapter 6. CMU: From the General Problem Solver to the Physical Symbol System and Production Systems Chapter 7. Stanford University and SRI Part III. The Challenges of 1970 Chapter 8. The Mansfield Amendment, “The Heilmeier Era”, and the Crisis in Research Funding Chapter 9. The AI Culture Wars: the War Inside AI and Academia Chapter 10. The AI Culture Wars: Popular Culture Part IV. Big Ideas and Hardware Improvements in the 1970s invert these and put the hardware chapter first Chapter 11. AI at MIT in the 1970s: The Semantic Fallout of NLR and Vision Chapter 12. Hardware, Software, and Applications in the 1970s Chapter 13. -
IKRIS Scenarios Inter-Theory (ISIT)
IKRIS Scenarios Inter-Theory (ISIT) Jerry Hobbs with the IKRIS Scenarios Working Group Contributions from Danny Bobrow, Chris Deaton, Mike Gruninger, Pat Hayes, Arun Majumdar, David Martin, Drew McDermott, Sheila McIlraith, Karen Myers, David Morley, John Sowa, Marco Valtorta, and Chris Welty. IKRIS Scenarios Fundamentals Events Most ontologies dealing with processes or scenarios will have something corresponding to events. PSL uses the term "activity_occurrence" for events. Cyc uses the term "Event". Other possible terms have drawbacks. "Action" connotes an event that has an agent, so it is only a subclass of events. "Process" and "Procedure" connote complex events that have subevents, and we need a term that will cover primitive events as well. One can argue that "activity" and "activity occurrence" have the same problem. For these reasons, we have chosen the term "Events". Types and Tokens We can refer to both event types and event tokens. PSL uses the terms "activity" and "activity_occurrence" for these two. Cyc uses the terms "EventType" and "Event". Another possible pair of term is "EventClass" and "EventInstance", but this biases one toward interpretation of types as classes, which may not be desirable (see below). It may seem desirable to make the type-token distinction explicit in the terms, and thus use "EventType" and "EventToken". But as Pat Hayes has pointed out, an event token may be a token of many types of events. A specific event may be a token of a running event type, an exercising event type, a winning event type, and numerous other types. Essentially, "token" is a relation between specific events and event types. -
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. -
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. -
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. -
' MACSYMA Users' Conference
' MACSYMA Users'Conference Held at University of California Berkeley, California July 27-29, 1977 I TECH LIBRARY KAFB, NY NASA CP-2012 Proceedings of the 1977 MACSYMA Users’ Conference Sponsored by Massachusetts Institute of Technology, University of California at Berkeley, NASA Langley Research Center and held at Berkeley, California July 27-29, 1977 Scientific and TechnicalInformation Office 1977 NATIONALAERONAUTICS AND SPACE ADMINISTRATION NA5A Washington, D.C. FOREWORD The technical programof the 1977 MACSPMA Users' Conference, held at Berkeley,California, from July 27 to July 29, 1977, consisted of the 45 contributedpapers reported in.this publicationand of a workshop.The work- shop was designed to promote an exchange of information between implementers and users of the MACSYMA computersystem and to help guide future developments. I The response to the call for papers has well exceeded the early estimates of the conference organizers; and the high quality and broad ra.ngeof topics of thepapers submitted has been most satisfying. A bibliography of papers concerned with the MACSYMA system is included at the endof this publication. We would like to thank the members of the programcommittee, the many referees, and the secretarial and technical staffs at the University of California at Berkeley and at the Laboratory for Computer Science, Massachusetts Instituteof Technology, for shepherding the many papersthrough the submission- to-publicationprocess. We are especiallyappreciative of theburden. carried by .V. Ellen Lewis of M. I. T. for serving as expert in document preparation from computer-readableto camera-ready copy for several papers. This conference originated as the result of an organizing session called by Joel Moses of M.I.T.