John Mccarthy 1927-2011
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Mccarthy Variations in a Modal Key ∗ Johan Van Benthem A,B
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Artificial Intelligence 175 (2011) 428–439 Contents lists available at ScienceDirect Artificial Intelligence www.elsevier.com/locate/artint McCarthy variations in a modal key ∗ Johan van Benthem a,b, a University of Amsterdam, Institute for Logic, Language and Computation (ILLC), P.O. Box 94242, Amsterdam, GE, Netherlands b Stanford, United States article info abstract Article history: We take a fresh look at some major strands in John McCarthy’s work from a logician’s Available online 3 April 2010 perspective. First, we re-analyze circumscription in dynamic logics of belief change under hard and soft information. Next, we re-analyze the regression method in the Situation Keywords: Calculus in terms of update axioms for dynamic–epistemic temporal logics. Finally, we Circumscription draw some general methodological comparisons between ‘Logical AI’ and practices in Fixed-point logic Structural rules modal logic, pointing at some creative tensions. Belief change © 2010 Elsevier B.V. All rights reserved. Situation Calculus Temporal logic Regression method Dynamic epistemic logic 1. Introduction John McCarthy is a colleague whom I have long respected and admired. He is a truly free spirit who always commu- nicates about issues in an open-minded way, away from beaten academic tracks and entrenched academic ranks. Each encounter has been a pleasure, from being involved with his creative Ph.D. students in Logical AI (each a remarkable char- acter) to his lively presence at our CSLI workshop series on Logic, Language and Computation – and most recently, his participation in the Handbook of the Philosophy of Information [1]. -
Spring D85 Newsletter
Dartmouth Class of 1985 Spring 2016 SHADES OF GREEN Class Projects Make a Diference! Spotlight on Dartmouth Center for Professional Development Internships Through Class Projects, the Class of 1985 is also a sponsor of DCPD internships in a variety of professions. Read what one of our grateful interns has to say: Dear Class of 1985 Donor, Thank you so much for your contribution that funded my unpaid internship this winter! I interned as a so!ware developer at a tech start-up ca#ed Parent Co. in Burlington, VT, and I rea#y Are we officially middle aged? If 40 was the new 20 (recall our hopeful 20th enjoyed my experience there! reunion theme), is it time to begin our midlife crisis now? (Might be a great Parent Co. is the creator of Notabli, a social topic for our next issue…!) But whether due to crisis, or to sheer media app that a#ows parents to more securely share digital keepsakes (photos, videos, etc.) with confidence, conviction, and courage, some of us have stepped off the career &iends and family. I worked on the Android and path first chosen. For a few it’s been a completely new direction, and their web versions of Notabli for the duration of my stories are as compelling as their talents run wide and deep. internship. I had never worked with Android before, so I learned a great deal under the Erma Bombeck once said that “it takes a lot of courage to show your dreams mentorship of a very experienced Android to someone else.” We are grateful that the classmates featured here were developer who works for the company. -
Artificial Intelligence Goddard Space Flight Center | November 27-29, 2018 NASA Goddard Artificial Intelligence Workshop
National Aeronautics and Program Book Space Administration Lorem ipsum dolor sit amet, consectetuer sodales sagittis magna. Sed consequat, leo eget adipiscing elit. Aenean commodo ligula eget dolor. bibendum sodales, augue velit cursus nunc, Aenean massa. Cum sociis natoque penatibus et Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus magnis dis parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec, pellentesque mus. Donec quam felis, ultricies nec, pellentesque eu, pretium quis, sem. Nulla consequat massa eu, pretium quis, sem. Nulla consequat massa quis enim. Donec pede justo, fringilla vel, aliquet quis enim. Donec pede justo, fringilla vel, aliquet nec, vulputate eget, arcu. In enim justo, rhoncus nec, vulputate eget, arcu. In enim justo, rhoncus ut, imperdiet a, venenatis vitae, justo. Nullam ut, imperdiet a, venenatis vitae, justo. Nullam dictum felis eu pede mollis pretium. Integer dictum felis eu pede mollis pretium. Integer tincidunt. Cras dapibus. Vivamus elementum tincidunt. Cras dapibus. Vivamus elementum semper nisi. Aenean vulputate eleifend tellus. semper nisi. Aenean vulputate eleifend tellus. Aenean leo ligula, porttitor eu, consequat vitae, Aenean leo ligula, porttitor eu, consequat vitae, eleifend ac, enim. Aliquam lorem ante, dapibus eleifend ac, enim. Aliquam lorem ante, dapibus in, viverra quis, feugiat a, tellus. Phasellus viverra in, viverra quis, feugiat a, tellus. Phasellus viverra nulla ut metus varius laoreet. Quisque rutrum. nulla ut metus varius laoreet. Quisque rutrum. Aenean imperdiet. Etiam ultricies nisi vel augue. Aenean imperdiet. Etiam ultricies nisi vel augue. Curabitur ullamcorper ultricies nisi. Nam eget Curabitur ullamcorper ultricies nisi. Nam eget dui. dui. Etiam rhoncus. Maecenas tempus, tellus Etiam rhoncus. -
A Logic-Based Calculus of Events
10 A Logic-based Calculus of Events ROBERT KOWALSKI and MAREK SERGOT 1 introduction Formal Logic can be used to represent knowledge of many kinds for many purposes. It can be used to formalize programs, program specifications, databases, legislation, and natural language in general. For many such applications of logic a representation of time is necessary. Although there have been several attempts to formalize the notion of time in classical first-order logic, it is still widely believed that classical logic is not adequate for the representation of time and that some form of non-classical Temporal Logic is needed. In this paper, we shall outline a treatment of time, based on the notion of event, formalized in the Horn clause subset of classical logic augmented with negation as failure. The resulting formalization is executable as a logic program. We use the term ‘‘event calculus’’ to relate it to the well-known ‘‘situation calculus’’ (McCarthy and Hayes 1969). The main difference between the two is conceptual: the situation calculus deals with global states whereas the event calculus deals with local events and time periods. Like the event calculus, the situation calculus can be formalized by means of Horn clauses augmented with negation by failure (Kowalski 1979). The main intended applications investigated in this paper are the updating of data- bases and narrative understanding. In order to treat both cases uniformly we have taken the view that an update consists of the addition of new knowledge to a knowledge base. The effect of explicit deletion of information in conventional databases is obtained without deletion by adding new knowledge about the end of the period of time for which the information holds. -
ARTIFICIAL INTELLIGENCE for Executives and Policy Makers
DINO ESPOSITO SIMONE MASSARO ARTIFICIAL INTELLIGENCE for executives and policy makers Vision and tools to predict the unpredictable Dino Esposito Simone Massaro ARTIFICIAL INTELLIGENCE for executives and policy makers Vision and tools to predict the unpredictable Dino Esposito, Simone Massaro ARTIFICIAL INTELLIGENCE for executives and policy makers Vision and tools to predict the unpredictable © Dino Esposito, 2019 © Simone Massaro, 2019 No part of this publication may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into any language or computer language, in any form or by any means, electronic, mechanical, magnetic, optical, chemical, manual or otherwise, without the prior written permission of one of the authors. A monumental Thank You goes to the engineering team at For a deeper technical overview of machine learning algorithms BaxEnergy for their general support and the effort put in reviewing and programming techniques, including a basic explanation of the the text. mathematics behind and some programming examples in Python and .NET, here’s a further reference. An even bigger Thank You is reserved to a couple of special people for their relentless help in making sense of the trickiest aspects of machine learning algorithms: Francesco Esposito (Youbiquitous) and Tiago Santos (Smartwatt). Introducing Machine Learning If you will find this reading easy and pleasant, well, that was because Dino Esposito, Francesco Esposito of them! If you still find it hard, well, that’s only our fault. Microsoft Press, 2019 ISBN-10: 0135565669 ISBN-13: 978-0135565667 Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. -
Reconciling Situation Calculus and Fluent Calculus
Reconciling Situation Calculus and Fluent Calculus Stephan Schiffel and Michael Thielscher Department of Computer Science Dresden University of Technology stephan.schiffel,mit @inf.tu-dresden.de { } Abstract between domain descriptions in both calculi. Another ben- efit of a formal relation between the SC and the FC is the The Situation Calculus and the Fluent Calculus are successful possibility to compare extensions made separately for the action formalisms that share many concepts. But until now two calculi, such as concurrency. Furthermore it might even there is no formal relation between the two calculi that would allow to formally analyze the relationship between the two allow to translate current or future extensions made only for approaches as well as between the programming languages one of the calculi to the other. based on them, Golog and FLUX. Furthermore, such a formal Golog is a programming language for intelligent agents relation would allow to combine Golog and FLUX and to ana- that combines elements from classical programming (condi- lyze which of the underlying computation principles is better tionals, loops, etc.) with reasoning about actions. Primitive suited for different classes of programs. We develop a for- statements in Golog programs are actions to be performed mal translation between domain axiomatizations of the Situ- by the agent. Conditional statements in Golog are composed ation Calculus and the Fluent Calculus and present a Fluent of fluents. The execution of a Golog program requires to Calculus semantics for Golog programs. For domains with reason about the effects of the actions the agent performs, deterministic actions our approach allows an automatic trans- lation of Golog domain descriptions and execution of Golog in order to determine the values of fluents when evaluating programs with FLUX. -
David Kotz Vita
David Kotz Department of Computer Science www.cs.dartmouth.edu/∼kotz Dartmouth College kotz at dartmouth.edu 6211 Sudikoff Laboratory +1 603–646-1439 (direct) Hanover, NH 03755-3510 +1 603–646-2206 (main) LinkedIn September 19, 2021 Education Ph.D Computer Science Duke University 1991 B.A. Computer Science and Physics Dartmouth College 1986 Positions Dartmouth College, Administration 2021– Interim Provost 2017–2018 Interim Provost (11 months) 2009–2015 Associate Dean of the Faculty for the Sciences (six years) Dartmouth College, Research leadership 2016– Core Director (Emerging Technologies and Data Analytics), Center for Technology and Behavioral Health 2004–2007 Executive Director, Institute for Security Technology Studies 2003–2004 Director of Research and Development, Institute for Security Technology Studies Dartmouth College, Faculty: Department of Computer Science 2020– Pat and John Rosenwald Professor (endowed chair) 2019–2020 International Paper Professor (endowed chair) 2010–2019 Champion International Professor (endowed chair) 2003– Professor 1997–2003 Associate Professor 1991–1997 Assistant Professor Visiting positions 2019–2020 Visiting Professor in the Center for Digital Health Interventions at ETH Zurich,¨ Switzerland 2008–2009 Fulbright Research Scholar at the Indian Institute of Science (IISc), Bangalore, India Executive summary David Kotz is the Interim Provost, the Pat and John Rosenwald Professor in the Department of Computer Science, and the Director of Emerging Technologies and Data Analytics in the Center for Technology and Behavioral Health, all at Dartmouth College. He previously served as Associate Dean of the Faculty for the Sciences and as the Executive Director of the Institute for Security Technology Studies. His research interests include security and privacy in smart homes, pervasive computing for healthcare, and wireless networks. -
Reconciling the Event Calculus with the Situation Calculus
1 In The Journal of Logic Programming, Special Issue: reasoning about action and change, 31(1-3), Pages 39-58, 1997. Reconciling the Event Calculus with the Situation Calculus Robert Kowalski and Fariba Sadri Department of Computing, Imperial College, 180 Queen's Gate, London SW7 2BZ Emails: [email protected] [email protected] Abstract In this paper, to compare the situation calculus and event calculus we formulate both as logic programs and prove properties of these by reasoning with their completions augmented with induction. We thus show that the situation calculus and event calculus imply one another. Whereas our derivation of the event calculus from the situation calculus requires the use of induction, our derivation of the situation calculus from the event calculus does not. We also show that in certain concrete applications, such as the missing car example, conclusions that seem to require the use of induction in the situation calculus can be derived without induction in the event calculus. To compare the two calculi, we need to make a number of small modifications to both. As a by-product of these modifications, the resulting calculi can be used to reason about both actual and hypothetical states of affairs, including counterfactual ones. We further show how the core axioms of both calculi can be extended to deal with domain or state constraints and certain types of ramifications. We illustrate this by examples from legislation and the blocks world. Keywords: temporal reasoning, event calculus, situation calculus, actual and hypothetical events 1 Introduction In this paper we revisit our earlier comparison of the situation calculus and event calculus [11] using a more general approach to the representations of situations, in the spirit of Van Belleghem, Denecker and De Schreye [20, 21]. -
Actions and Other Events in Situation Calculus
ACTIONS AND OTHER EVENTS IN SITUATION CALCULUS John McCarthy Computer Science Department Stanford University Stanford, CA 94305 [email protected] http://www-formal.stanford.edu/jmc/ Abstract vention one situation at a time. Then we offer a general viewpoint on the sit- This article presents a situation calculus for- uation calculus and its applications to real malism featuring events as primary and the world problems. It relates the formalism of usual actions as a special case. Events that [MH69] which regards a situation as a snap- are not actions are called internal events and shot of the world to situation calculus theo- actions are called external events. The effects ries involving only a few fluents. of both kinds of events are given by effect axioms of the usual kind. The actions are assumed to be performed by an agent as is 1 Introduction: Actions and other usual in situation calculus. An internal event events e occurs in situations satisfying an occurrence assertion for that event. This article emphasizes the idea that an action by an A formalism involving actions and internal agent is a particular kind of event. The idea of event events describes what happens in the world is primary and an action is a special case. The treat- more naturally than the usual formulations ment is simpler than those regarding events as natural involving only actions supplemented by state actions. constraints. Ours uses only ordinary logic without special causal implications. It also The main features of our treatment are as follows. seems to be more elaboration tolerant. -
Digitalization Transformation Guides
ISSUE Royal Digitalization Transformation 02 Academy of February 2020 Engineering Guides 1. In this issue: Solutions for a • What is Artificial Intelligence? New Economy • How to bring benefit from Machine Learning in industry? There are numerous applications in • Dangers and ethical which Machine considerations of AI Learning is the right solution and can bring benefit to the Industrial Implementation of Artificial1. Dangers andIntelligent ethical industry. Systems in Industry considerations. 3 . Trends & New Software P.4 The prominence of AI is demonstrated in popular culture with television and movies. Artificial Intelligence refers to that of human intelligence demonstrated by machines which are programmed to think like humans and replicate decisions, behaviours and actions. The application of AI is widely associated to machines having the capability to learn and to solve problems as humans would, or to a better degree. Machine Learning (ML), and AI in general, are still relatively new research fields and are not necessarily the solution to every technological and scientific problem. While it is expected that the presence of AI in the industry will largely increase in the near future, there is a broad spectrum of problems for which AI may not be yet a satisfactory solution. Contents WHAT IS ARTIFICIAL INTELLIGENCE? 3 BRIEF HISTORY 3 DIFFERENCE BETWEEN AI AND MACHINE LEARNING (ML) 3 ML BASICS 4 TAXONOMY OF ML TECHNIQUES 4 LIMITATIONS OF ML 6 THE ML LANDSCAPE BEYOND THE HYPE 6 HOW TO BRING BENEFIT FROM ML IN INDUSTRY? 7 DANGERS AND ETHICAL CONSIDERATIONS. 8 What is Artificial Intelligence? Brief History Although not generally reported, formal research on Artificial Intelligence can be said to have originated at the Macy conferences on Cybernetics (1945-1953). -
Situation Calculus
Institute for Software Technology Situation Calculus Gerald Steinbauer Institute for Software Technology Gerald Steinbauer Situation Calculus - Introduction 1 Institute for Software Technology Organizational Issues • Dates – 05.10.2017 8:45-11:00 (HS i12) lecture and first assignment – 12.10.2017 8:45-11:00 (HS i12) lecture and programming assignment – 11.10.2017 18:00-18:45 (HS i11) practice – 18.10.2017 18:00-18:45 (HS i11) practice and solution for first assignment – 16.10.2017 12:00 (office IST) submission first assignment – 09.11.2016 23:59 (group SVN) submission programming assignment Gerald Steinbauer Situation Calculus - Introduction 2 Institute for Software Technology Agenda • Organizational Issues • Motivation • Introduction – Brief Recap of First Order Logic (if needed) • Situation Calculus (today) – Introduction – Formal Definition – Usage • Programming with Situation Calculus (next week) – Implementation – Domain Modeling Gerald Steinbauer Situation Calculus - Introduction 3 Institute for Software Technology Literature “Knowledge in Action” “Artificial Intelligence: A by Raymond Reiter Modern Approach” MIT Press by Stuart Russel amd Peter Norvig Prentice Hall Gerald Steinbauer Situation Calculus - Introduction 4 Institute for Software Technology Motivation What is Situation Calculus good for? Gerald Steinbauer Situation Calculus - Introduction 5 Institute for Software Technology There is nothing permanent except change. Heraclitus of Ephesus, 535–c. 475 BC Gerald Steinbauer Situation Calculus - Introduction 6 Institute for -
Beyond Minimizing Change
From: AAAI-97 Proceedings. Copyright © 1997, AAAI (www.aaai.org). All rights reserved. eyond imizing C Tom Costello Dept of Computer Science, Stanford, CA 94305 costelloQcs.Stanford.EDU Abstract Problem, the Stanford Murder Mystery, the Ferry Boat Problem, and many other problems were posed as counter examples to proposed solutions. This led researchers to look for ways to validate their theories. One of the first ways was proposed by Lin and Shoham (1995), where they proposed reducing a non-monotonic theory to a monotonic theory to show correctness. Lifschitz and Gelfond suggested reducing reasoning about action to well understood models like automata, and developed a language A well suited to describing automata as they arise in action. This work has been greatly extended, and many solutions have been shown equivalent under certain assumptions. Kartha studied the relationship between various dif- ferent proposals, and showed that under certain syn- tactic assumptions, that many solutions were equiva- lent to those solutions based on A. Lifschitz has considered certain properties, like stability under definitional extension, and restricted monotonicity, that he claims all approaches to the frame problem should obey. Sandewall (Sandewall 1994) proposed justifying non-monotonic treatments by reducing them to dynamical systems. We propose another way of studying non-monotonic treatments of actions, though the methodology is not restricted to actions. We consider what elaborations, Hntroduetion or changes, the treatment allows, in terms of conjoining information. Reasoning about action has been one of the favorite In this paper we shall keep to as simple a language domains for non-monotonic reasoning. One of the as possible.