From Naturalism to Psychologism
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Deduction Systems Based on Resolution, We Limit the Following Considerations to Transition Systems Based on Analytic Calculi
Author’s Address Norbert Eisinger European Computer–Industry Research Centre, ECRC Arabellastr. 17 D-8000 M¨unchen 81 F. R. Germany [email protected] and Hans J¨urgen Ohlbach Max–Planck–Institut f¨ur Informatik Im Stadtwald D-6600 Saarbr¨ucken 11 F. R. Germany [email protected] Publication Notes This report appears as chapter 4 in Dov Gabbay (ed.): ‘Handbook of Logic in Artificial Intelligence and Logic Programming, Volume I: Logical Foundations’. It will be published by Oxford University Press, 1992. Fragments of the material already appeared in chapter two of Bl¨asis & B¨urckert: Deduction Systems in Artificial Intelligence, Ellis Horwood Series in Artificial Intelligence, 1989. A draft version has been published as SEKI Report SR-90-12. The report is also published as an internal technical report of ECRC, Munich. Acknowledgements The writing of the chapters for the handbook has been a highly coordinated effort of all the people involved. We want to express our gratitude for their many helpful contributions, which unfortunately are impossible to list exhaustively. Special thanks for reading earlier drafts and giving us detailed feedback, go to our second reader, Bob Kowalski, and to Wolfgang Bibel, Elmar Eder, Melvin Fitting, Donald W. Loveland, David Plaisted, and J¨org Siekmann. Work on this chapter started when both of us were members of the Markgraf Karl group at the Universit¨at Kaiserslautern, Germany. With our former colleagues there we had countless fruitful discus- sions, which, again, cannot be credited in detail. During that time this research was supported by the “Sonderforschungsbereich 314, K¨unstliche Intelligenz” of the Deutsche Forschungsgemeinschaft (DFG). -
Domain Theory and the Logic of Observable Properties
Domain Theory and the Logic of Observable Properties Samson Abramsky Submitted for the degree of Doctor of Philosophy Queen Mary College University of London October 31st 1987 Abstract The mathematical framework of Stone duality is used to synthesize a number of hitherto separate developments in Theoretical Computer Science: • Domain Theory, the mathematical theory of computation introduced by Scott as a foundation for denotational semantics. • The theory of concurrency and systems behaviour developed by Milner, Hennessy et al. based on operational semantics. • Logics of programs. Stone duality provides a junction between semantics (spaces of points = denotations of computational processes) and logics (lattices of properties of processes). Moreover, the underlying logic is geometric, which can be com- putationally interpreted as the logic of observable properties—i.e. properties which can be determined to hold of a process on the basis of a finite amount of information about its execution. These ideas lead to the following programme: 1. A metalanguage is introduced, comprising • types = universes of discourse for various computational situa- tions. • terms = programs = syntactic intensions for models or points. 2. A standard denotational interpretation of the metalanguage is given, assigning domains to types and domain elements to terms. 3. The metalanguage is also given a logical interpretation, in which types are interpreted as propositional theories and terms are interpreted via a program logic, which axiomatizes the properties they satisfy. 2 4. The two interpretations are related by showing that they are Stone duals of each other. Hence, semantics and logic are guaranteed to be in harmony with each other, and in fact each determines the other up to isomorphism. -
Probabilistic Logic Programming
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Lirias ProbLog2: Probabilistic logic programming Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer, and Luc De Raedt KU Leuven, Belgium, [email protected], https://dtai.cs.kuleuven.be/problog Abstract. We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system's components. Keywords: probabilistic programming, probabilistic inference, param- eter learning 1 Introduction Probabilistic programming is an emerging subfield of artificial intelligence that extends traditional programming languages with primitives to support proba- bilistic inference and learning. Probabilistic programming is closely related to statistical relational learning (SRL) but focusses on a programming language perspective rather than on a graphical model one. The common goal is to pro- vide powerful tools for modeling of and reasoning about structured, uncertain domains that naturally arise in applications such as natural language processing, bioinformatics, and activity recognition. This demo presents the ProbLog2 system, the state of the art implementa- tion of the probabilistic logic programming language ProbLog [2{4]. -
Probabilistic Boolean Logic, Arithmetic and Architectures
PROBABILISTIC BOOLEAN LOGIC, ARITHMETIC AND ARCHITECTURES A Thesis Presented to The Academic Faculty by Lakshmi Narasimhan Barath Chakrapani In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the School of Computer Science, College of Computing Georgia Institute of Technology December 2008 PROBABILISTIC BOOLEAN LOGIC, ARITHMETIC AND ARCHITECTURES Approved by: Professor Krishna V. Palem, Advisor Professor Trevor Mudge School of Computer Science, College Department of Electrical Engineering of Computing and Computer Science Georgia Institute of Technology University of Michigan, Ann Arbor Professor Sung Kyu Lim Professor Sudhakar Yalamanchili School of Electrical and Computer School of Electrical and Computer Engineering Engineering Georgia Institute of Technology Georgia Institute of Technology Professor Gabriel H. Loh Date Approved: 24 March 2008 College of Computing Georgia Institute of Technology To my parents The source of my existence, inspiration and strength. iii ACKNOWLEDGEMENTS आचायातर् ्पादमादे पादं िशंयः ःवमेधया। पादं सॄचारयः पादं कालबमेणच॥ “One fourth (of knowledge) from the teacher, one fourth from self study, one fourth from fellow students and one fourth in due time” 1 Many people have played a profound role in the successful completion of this disser- tation and I first apologize to those whose help I might have failed to acknowledge. I express my sincere gratitude for everything you have done for me. I express my gratitude to Professor Krisha V. Palem, for his energy, support and guidance throughout the course of my graduate studies. Several key results per- taining to the semantic model and the properties of probabilistic Boolean logic were due to his brilliant insights. -
A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability and Statistics
FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 FLORENTIN SMARANDACHE A UNIFYING FIELD IN LOGICS: NEUTROSOPHIC LOGIC. NEUTROSOPHY, NEUTROSOPHIC SET, NEUTROSOPHIC PROBABILITY AND STATISTICS (fourth edition) NL(A1 A2) = ( T1 ({1}T2) T2 ({1}T1) T1T2 ({1}T1) ({1}T2), I1 ({1}I2) I2 ({1}I1) I1 I2 ({1}I1) ({1} I2), F1 ({1}F2) F2 ({1} F1) F1 F2 ({1}F1) ({1}F2) ). NL(A1 A2) = ( {1}T1T1T2, {1}I1I1I2, {1}F1F1F2 ). NL(A1 A2) = ( ({1}T1T1T2) ({1}T2T1T2), ({1} I1 I1 I2) ({1}I2 I1 I2), ({1}F1F1 F2) ({1}F2F1 F2) ). ISBN 978-1-59973-080-6 American Research Press Rehoboth 1998, 2000, 2003, 2005 1 Contents: Preface by C. Le: 3 0. Introduction: 9 1. Neutrosophy - a new branch of philosophy: 15 2. Neutrosophic Logic - a unifying field in logics: 90 3. Neutrosophic Set - a unifying field in sets: 125 4. Neutrosophic Probability - a generalization of classical and imprecise probabilities - and Neutrosophic Statistics: 129 5. Addenda: Definitions derived from Neutrosophics: 133 2 Preface to Neutrosophy and Neutrosophic Logic by C. -
Husserl's Psychologism, and Critique of Psychologism, Revisited
Husserl Studies 2006. DOI 10.1007/s10743-006-9008-5 Ó Springer 2006 HusserlÕs Psychologism, and Critique of Psychologism, Revisited BURT C. HOPKINS Department of Philosophy, Seattle University, Seattle, WA, 98122-4460, USA 1. Introduction HusserlÕs mature statement of his views on the nature of psycholo- gism and on the four decade phenomenological ‘‘war’’1 against it identifies three kinds: logical, epistemological, and transcendental psy- chologism. In what follows, I argue that the psychologism of Hus- serlÕs earliest work, The Philosophy of Arithmetic,2 does not correspond to any of these three types identified in Formal and Tran- scendental Logic. I show that this lack of correspondence is signifi- cant, and not only because HusserlÕs final account of how phenomenology overcomes the three kinds of psychologism identified in FTL does not address the kind of psychologism that characterizes the PA. Beyond this, I also show that this accountÕs appeal to the numerical identity of the objects of thought, as the definitive mark of their trans-psychological status, represents an appeal to the very same logical structure that the psychologism in the PA attempted, unsuc- cessfully, to account for. That is, I show that the logical structure of the ‘‘one over many’’ unity belonging to the ‘‘authentic’’ cardinal numbers (Anzahlen) investigated in the PA also characterizes the unity of the ‘‘numerical identity’’ appealed to in HusserlÕs account (in FTL and Experience and Judgment) of phenomenologyÕs victory over psy- chologism. I conclude my remarks -
Are Dogs Logical Animals ? Jean-Yves Beziau University of Brazil, Rio De Janeiro (UFRJ) Brazilian Research Council (Cnpq) Brazilian Academy of Philosophy (ABF)
Are Dogs Logical Animals ? Jean-Yves Beziau University of Brazil, Rio de Janeiro (UFRJ) Brazilian Research Council (CNPq) Brazilian Academy of Philosophy (ABF) Autre être fidèle du tonneau sans loques malgré ses apparitions souvent loufoques le chien naît pas con platement idiot ne se laisse pas si facilement mener en rat d’eau il ne les comprend certes pas forcément toutes à la première coupe ne sait pas sans l’ombre deux doutes la fière différance antre un loup et une loupe tout de foi à la croisade des parchemins il saura au non du hasard dès trousser la chienlit et non en vain reconnaître celle qui l’Amen à Rhum sans faim Baron Jean Bon de Chambourcy 1. Human beings and other animals In Ancient Greece human beings have been characterized as logical animals. This traditional catching is a bit lost or/and nebulous. What this precisely means is not necessarily completely clear because the word Logos has four important aspects: language, science, reasoning, relation. The Latinized version of “logical”, i.e. “rational”, does not fully preserve these four meanings. Moreover later on human beings were qualified as “homo sapiens” (see Beziau 2017). In this paper we will try to clarify the meaning of “logical animals” by examining if dogs can also be considered logical animals. An upside question of “Are dogs logical animals?” is: “Are human beings the only rational animals?”. It is one thing to claim that human beings are logical animals, but another thing is to super claim that human beings are the only logical animals. -
Maurice Finocchiaro Discusses the Lessons and the Cultural Repercussions of Galileo’S Telescopic Discoveries.” Physics World, Vol
MAURICE A. FINOCCHIARO: CURRICULUM VITAE CONTENTS: §0. Summary and Highlights; §1. Miscellaneous Details; §2. Teaching Experience; §3. Major Awards and Honors; §4. Publications: Books; §5. Publications: Articles, Chapters, and Discussions; §6. Publications: Book Reviews; §7. Publications: Proceedings, Abstracts, Translations, Reprints, Popular Media, etc.; §8. Major Lectures at Scholarly Meetings: Keynote, Invited, Funded, Honorarium, etc.; §9. Other Lectures at Scholarly Meetings; §10. Public Lectures; §11. Research Activities: Out-of-Town Libraries, Archives, and Universities; §12. Professional Service: Journal Editorial Boards; §13. Professional Service: Refereeing; §14. Professional Service: Miscellaneous; §15. Community Service §0. SUMMARY AND HIGHLIGHTS Address: Department of Philosophy; University of Nevada, Las Vegas; Box 455028; Las Vegas, NV 89154-5028. Education: B.S., 1964, Massachusetts Institute of Technology; Ph.D., 1969, University of California, Berkeley. Position: Distinguished Professor of Philosophy, Emeritus; University of Nevada, Las Vegas. Previous Positions: UNLV: Assistant Professor, 1970-74; Associate Professor, 1974-77; Full Professor, 1977-91; Distinguished Professor, 1991-2003; Department Chair, 1989-2000. Major Awards and Honors: 1976-77 National Science Foundation; one-year grant; project “Galileo and the Art of Reasoning.” 1983-84 National Endowment for the Humanities, one-year Fellowship for College Teachers; project “Gramsci and the History of Dialectical Thought.” 1987 Delivered the Fourth Evert Willem Beth Lecture, sponsored by the Evert Willem Beth Foundation, a committee of the Royal Netherlands Academy of Sciences, at the Universities of Amsterdam and of Groningen. 1991-92 American Council of Learned Societies; one-year fellowship; project “Democratic Elitism in Mosca and Gramsci.” 1992-95 NEH; 3-year grant; project “Galileo on the World Systems.” 1993 State of Nevada, Board of Regents’ Researcher Award. -
1 Psychologism Revisited
1 Psychologism Revisited Although at one time it was quite usual to suppose that the principles of logic are “the laws of thought” . , Frege’s vigorous critique was so influential that there has been rather little support, of late, for “psychologism” in any shape or form. However, Frege’s arguments against psychologism are, I suspect, less conclusive, and at least some form of psychologism more plausible, than it is nowadays fashionable to suppose. —Susan Haack1 1.0 Introduction In this chapter I revisit the late nineteenth- and early twentieth-century debate about logical psychologism. It is clear that this debate significantly determined the subsequent development of philosophy and psychology alike. Neither the emergence of analytic philosophy from Kant’s idealism2 nor the emergence of experimental or scientific psychology from Brentano’s phenomenology3 could have occurred without it. It is also clear that Frege and Husserl routed the “psychologicists.” What is much less clear, and what I want critically to rethink and reformulate, is the philosophical upshot of this seminal controversy. In section 1.1, I look at what Frege and Husserl say about and against log- ical psychologism. Logical psychologism boils down to the thesis that logic is explanatorily reducible to empirical psychology. Identifying a cogent Fregean or Husserlian argument against psychologism proves to be difficult, however, because their antipsychologistic arguments are question-begging. In section 1.2, I propose that logical psychologism can be most accurately construed as a species of scientific naturalism, and more particularly as a form of scientific naturalism about logic. If logical psychologism is a form of scientific naturalism about logic, then Frege’s and Husserl’s antipsycholo- gism is also a species of antinaturalism. -
Probabilistic Logic Programming and Bayesian Networks
Probabilistic Logic Programming and Bayesian Networks Liem Ngo and Peter Haddawy Department of Electrical Engineering and Computer Science University of WisconsinMilwaukee Milwaukee WI fliem haddawygcsuwmedu Abstract We present a probabilistic logic programming framework that allows the repre sentation of conditional probabilities While conditional probabilities are the most commonly used metho d for representing uncertainty in probabilistic exp ert systems they have b een largely neglected bywork in quantitative logic programming We de ne a xp oint theory declarativesemantics and pro of pro cedure for the new class of probabilistic logic programs Compared to other approaches to quantitative logic programming weprovide a true probabilistic framework with p otential applications in probabilistic exp ert systems and decision supp ort systems We also discuss the relation ship b etween such programs and Bayesian networks thus moving toward a unication of two ma jor approaches to automated reasoning To appear in Proceedings of the Asian Computing Science Conference Pathumthani Thailand December This work was partially supp orted by NSF grant IRI Intro duction Reasoning under uncertainty is a topic of great imp ortance to many areas of Computer Sci ence Of all approaches to reasoning under uncertainty probability theory has the strongest theoretical foundations In the quest to extend the framwork of logic programming to represent and reason with uncertain knowledge there havebeenseveral attempts to add numeric representations of uncertainty -
A Mistake on My Part, Volume 1, Pp 665-659 in We Will Show Them!
A Mistake on My Part Melvin Fitting 1 The Background I first met Dov at a logic conference in Manchester, in August 1969, though we had begun a mathematical correspondence the previous year. Here are a few photos from the conference. As I recall, Dov planned to get married shortly after the conference. I found a letter in my files mentioning that I sent Dov a copy of the pictures in 1969, so this is for everybody else. Figure 1 shows Dov at Jodrell Bank, the huge radio telescope complex run by the University of Manchester. Figure 1. 2 Melvin Fitting Figure 2 shows, from left to right, Saul Kripke, Dov, Michael Rabin, and someone I can’t identify. Figure 2. Figure 3 shows the following. Back row: David Pincus, Miriam Lucian, Dov, Saul Kripke; front row: George Rousseau, and me. It was also at this conference that a mistake I made got straightened out. I don’t know if it had an effect on Dov’s work, but it may have. But let’s discuss that in a section of its own. 2 Where I Went Wrong Kripke’s semantics for intuitionistic logic came along in [Kripke, 1965], and prompted a considerable amount of research. It was a possible worlds se- mantics, and in it domains for quantifiers varied from world to world, though subject to a monotonicity condition. It was a natural question: what would a restriction to constant domain intuitionistic models impose. In my dis- sertation, [Fitting, 1969], I had shown in passing that, for formulas without universal quantifiers, constant domain and monotonic domain semantics validated the same formulas (unlike in classical logic, the two quantifiers are not interdefinable intuitionistically). -
Generalising the Interaction Rules in Probabilistic Logic
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Generalising the Interaction Rules in Probabilistic Logic Arjen Hommersom and Peter J. F. Lucas Institute for Computing and Information Sciences Radboud University Nijmegen Nijmegen, The Netherlands {arjenh,peterl}@cs.ru.nl Abstract probabilistic logic hand in hand such that the resulting logic supports this double, logical and probabilistic, view. The re- The last two decades has seen the emergence of sulting class of probabilistic logics has the advantage that it many different probabilistic logics that use logi- can be used to gear a logic to the problem at hand. cal languages to specify, and sometimes reason, In the next section, we first review the basics of ProbLog, with probability distributions. Probabilistic log- which is followed by the development of the main methods ics that support reasoning with probability distri- used in generalising probabilistic Boolean interaction, and, butions, such as ProbLog, use an implicit definition finally, default logic is briefly discussed. We will use prob- of an interaction rule to combine probabilistic ev- abilistic Boolean interaction as a sound and generic, alge- idence about atoms. In this paper, we show that braic way to combine uncertain evidence, whereas default this interaction rule is an example of a more general logic will be used as our language to implement the interac- class of interactions that can be described by non- tion operators, again reflecting this double perspective on the monotonic logics. We furthermore show that such probabilistic logic. The new probabilistic logical framework local interactions about the probability of an atom is described in Section 3 and compared to other approaches can be described by convolution.