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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]. -
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. -
Scenarios for Using the ARPANET at the International Conference On
VAlirc NIC 11863 SCENARIOS for using the ARPANET at the INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION Washington, D.C. October 24—26, 1972 ARPA Network Information Center Stanford Research Institute Menlo Park, California 94025 , 11?>o - 3 £: 3c? - 16 $<}0-l!:}o 3 - & i 3o iW |{: 3 cp - 3 NIC 11863 SCENARIOS for using the ARPANET at the INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION Washington, D.C. October 24—26, 1972 ARPA Network Information Center Stanford Research Institute Menlo Park, California 94025 SCENARIOS FOR USING THE ARPANET AT THE ICCC We intend that the following scenarios be used by individuals to browse the ARPA Computer Network (ARPANET) in its current early stage of development and thereby to introduce themselves to some possibilities in computer communication. The scenarios include only a few of the existing ARPANET resources. They were chosen for this booklet (somewhat haphazardly) to exhibit variety and sophistication, while retaining simplicity. The scenarios are by no means complete or perfect. We have tried to make them accurate, but are certain that they contain errors. The scenarios are, therefore, only one kind of tool for experiencing computer communication. We assume that you will attend the various showings of film and videotape, pay close attention at the several scheduled demonstrations of specific resources, approach the ARPANET aggressively yourself using these scenarios, and unhesitatingly call upon the ICCC Special Project People for the advice and encouragement you are sure to need. The account numbers and passwords provided in these scenarios were generated spe cifically for the ICCC. It is hoped that some of them will remain available after the ICCC for continued browsing. -
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. -
Computer Chess Methods
COMPUTER CHESS METHODS T.A. Marsland Computing Science Department, University of Alberta, EDMONTON, Canada T6G 2H1 ABSTRACT Article prepared for the ENCYCLOPEDIA OF ARTIFICIAL INTELLIGENCE, S. Shapiro (editor), D. Eckroth (Managing Editor) to be published by John Wiley, 1987. Final draft; DO NOT REPRODUCE OR CIRCULATE. This copy is for review only. Please do not cite or copy. Acknowledgements Don Beal of London, Jaap van den Herik of Amsterdam and Hermann Kaindl of Vienna provided helpful responses to my request for de®nitions of technical terms. They and Peter Frey of Evanston also read and offered constructive criticism of the ®rst draft, thus helping improve the basic organization of the work. In addition, my many friends and colleagues, too numerous to mention individually, offered perceptive observations. To all these people, and to Ken Thompson for typesetting the chess diagrams, I offer my sincere thanks. The experimental work to support results in this article was possible through Canadian Natural Sciences and Engineering Research Council Grant A7902. December 15, 1990 COMPUTER CHESS METHODS T.A. Marsland Computing Science Department, University of Alberta, EDMONTON, Canada T6G 2H1 1. HISTORICAL PERSPECTIVE Of the early chess-playing machines the best known was exhibited by Baron von Kempelen of Vienna in 1769. Like its relations it was a conjurer's box and a grand hoax [1, 2]. In contrast, about 1890 a Spanish engineer, Torres y Quevedo, designed a true mechanical player for king-and-rook against king endgames. A later version of that machine was displayed at the Paris Exhibition of 1914 and now resides in a museum at Madrid's Polytechnic University [2]. -
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. -
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 -
CASE STUDY Chess: Deep Blue's Victory AI As Sport How Intelligent Is
AI as Sport In 1965 the Russian mathematician Alexander CASE STUDY Kronrod said, "Chess is the Drosophila of artificial intelligence." However, computer chess has developed as genetics might have if Chess: Deep Blue’s Victory the geneticists had concentrated their efforts starting in 1910 on breeding racing Drosophilia. We would have some science, but mainly we would have very fast fruit flies." - John McCarthy 1 2 How Intelligent is Deep Blue? On Game 2 (Game 2 - Deep Blue took an early lead. Kasparov resigned, but it turned out he could have forced a draw by perpetual check.) Saying Deep Blue doesn't really think about chess is like saying an airplane doesn't really This was real chess. This was a game any fly because it doesn't flap its wings. human grandmaster wouldhave been proud of. Joel Benjamin - Drew McDermott grandmaster, member Deep Blue team 3 4 Kasparov on Deep Blue Combinatorics of Chess Opening book 1996: Kasparov Beats Deep Blue Endgame • database of all 5 piece endgames exists; “I could feel --- I could smell --- a new kind of database of all 6 piece games being built intelligence acrossthe table.” Middle game • branching factor of 30 to 40 1997: Deep Blue Beats Kasparov • 1000(d/2) positions – 1 move by each player = 1,000 “Deep Blue hasn't proven anything.” – 2 moves by each player = 1,000,000 – 3 moves by each player = 1,000,000,000 5 6 1 Positions with Alpha-Beta Pruning History of Search Innovations Search Depth Positions Shannon, Turing Minimax search 1950 Kotok/McCarthy Alpha-beta pruning 1966 260MacHack Transposition -
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. -
Modern Masters of an Ancient Game
AI Magazine Volume 18 Number 4 (1997) (© AAAI) Modern Masters of an Ancient Game Carol McKenna Hamilton and Sara Hedberg he $100,000 Fredkin Prize for on Gary Kasparov in the final game of tute of Technology Computer Science Computer Chess, created in a tied, six-game match last May 11. Professor Edward Fredkin to encourage T1980 to honor the first program Kasparov had beaten the machine in continued research progress in com- to beat a reigning world chess champi- an earlier match held in February 1996. puter chess. The prize was three-tiered. on, was awarded to the inventors of The Fredkin Prize was awarded un- The first award of $5,000 was given to the Deep Blue chess machine Tuesday, der the auspices of AAAI; funds had two scientists from Bell Laboratories July 29, at the annual meeting of the been held in trust at Carnegie Mellon who in 1981 developed the first chess American Association for Artificial In- University. machine to achieve master status. telligence (AAAI) in Providence, The Fredkin Prize was originally es- Seven years later, the intermediate Rhode Island. tablished at Carnegie Mellon Universi- prize of $10,000 for the first chess ma- Deep Blue beat world chess champi- ty 17 years ago by Massachusetts Insti- chine to reach international master status was awarded in 1988 to five Carnegie Mellon graduate students who built Deep Thought, the precur- sor to Deep Blue, at the university. Soon thereafter, the team moved to IBM, where they have been ever since, working under wraps on Deep Blue. The $100,000 third tier of the prize was awarded at AAAI–97 to this IBM team, who built the first computer chess machine that beat a world chess champion. -
Chess As Played by Artificial Intelligence
Chess as Played by Artificial Intelligence Filemon Jankuloski, Adrijan Božinovski1 University American College of Skopje, Skopje, Macedonia [email protected] [email protected] Abstract. In this paper, we explore the evolution of how chess machinery plays chess. The purpose of the paper is to discuss the several different algorithms and methods which are implemented into chess machinery systems to either make chess playing more efficient or increase the playing strength of said chess machinery. Not only do we look at methods and algorithms implemented into chess machinery, but we also take a look at the many accomplishments achieved by these machines over a 70 year period. We will analyze chess machines from their conception up until their absolute domination of the competitive chess scene. This includes chess machines which utilize anything from brute force approaches to deep learning and neural networks. Finally, the paper is concluded by discussing the future of chess machinery and their involvement in both the casual and competitive chess scene. Keywords: Chess · Algorithm · Search · Artificial Intelligence 1 Introduction Artificial intelligence has become increasingly prevalent in our current society. We can find artificial intelligence being used in many aspects of our daily lives, such as through the use of Smartphones and autonomous driving vehicles such as the Tesla. There is no clear and concise definition for Artificial Intelligence, since it is a subject which still has massive space for improvement and has only recently been making most of its significant advancements. Leading textbooks on AI, however, define it as the study of “intelligent agents”, which can be represented by any device that perceives its environment and takes actions that maximizes its chances of achieving its goals [1].