J. Michael Dunn and Greg Restall
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Frontiers of Conditional Logic
City University of New York (CUNY) CUNY Academic Works All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects 2-2019 Frontiers of Conditional Logic Yale Weiss The Graduate Center, City University of New York How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_etds/2964 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Frontiers of Conditional Logic by Yale Weiss A dissertation submitted to the Graduate Faculty in Philosophy in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York 2019 ii c 2018 Yale Weiss All Rights Reserved iii This manuscript has been read and accepted by the Graduate Faculty in Philosophy in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy. Professor Gary Ostertag Date Chair of Examining Committee Professor Nickolas Pappas Date Executive Officer Professor Graham Priest Professor Melvin Fitting Professor Edwin Mares Professor Gary Ostertag Supervisory Committee The City University of New York iv Abstract Frontiers of Conditional Logic by Yale Weiss Adviser: Professor Graham Priest Conditional logics were originally developed for the purpose of modeling intuitively correct modes of reasoning involving conditional|especially counterfactual|expressions in natural language. While the debate over the logic of conditionals is as old as propositional logic, it was the development of worlds semantics for modal logic in the past century that cat- alyzed the rapid maturation of the field. -
Admissible Rules and Their Complexity
Admissible rules and their complexity Emil Jeřábek [email protected] http://math.cas.cz/~jerabek/ Institute of Mathematics of the Czech Academy of Sciences, Prague 24th Applications of Logic in Philosophy and the Foundations of Mathematics Szklarska Poręba, May 2019 Outline of the talks 1 Logics and admissibility 2 Transitive modal logics 3 Toy model: logics of bounded depth 4 Projective formulas 5 Admissibility in clx logics 6 Problems and complexity classes 7 Complexity of derivability 8 Complexity of admissibility Logics and admissibility 1 Logics and admissibility 2 Transitive modal logics 3 Toy model: logics of bounded depth 4 Projective formulas 5 Admissibility in clx logics 6 Problems and complexity classes 7 Complexity of derivability 8 Complexity of admissibility Propositional logics Propositional logic L: Language: formulas built from atoms x0; x1; x2;::: using a fixed set of finitary connectives Consequence relation: a relation Γ `L ' between sets of formulas and formulas s.t. I ' `L ' I Γ `L ' implies Γ; ∆ `L ' I Γ; ∆ `L ' and 8 2 ∆ Γ `L imply Γ `L ' I Γ `L ' implies σ(Γ) `L σ(') for every substitution σ Emil Jeřábek Admissible rules and their complexity ALPFM 2019, Szklarska Poręba 1:78 Unifiers and admissible rules Γ; ∆: finite sets of formulas L-unifier of Γ: substitution σ s.t. `L σ(') for all ' 2 Γ Single-conclusion rule: Γ = ' Multiple-conclusion rule: Γ = ∆ I Γ = ∆ is L-derivable (or valid) if Γ `L δ for some δ 2 ∆ I Γ = ∆ is L-admissible (written as Γ ∼L ∆) if every L-unifier of Γ also unifies some δ 2 ∆ NB: Γ is L-unifiable -
LOGIC BIBLIOGRAPHY up to 2008
LOGIC BIBLIOGRAPHY up to 2008 by L. Geldsetzer © Copyright reserved Download only for personal use permitted Heinrich Heine Universität Düsseldorf 2008 II Contents 1. Introductions 1 2. Dictionaries 3 3. Course Material 4 4. Handbooks 5 5. Readers 5 6. Bibliographies 6 7. Journals 7 8. History of Logic and Foundations of Mathematics 9 a. Gerneral 9 b. Antiquity 10 c. Chinese Antiquity 11 d. Scholastics 12 e. Islamic Medieval Scholastics 12 f. Modern Times 13 g. Contemporary 13 9. Classics of Logics 15 a. Antiquity 15 b. Medieval Scholastics 17 c. Modern and Recent Times 18 d. Indian Logic, History and Classics 25 10. Topics of Logic 27 1. Analogy and Metaphor, Likelihood 27 2. Argumentation, Argument 27 3. Axiom, Axiomatics 28 4. Belief, Believing 28 5. Calculus 29 8. Commensurability, see also: Incommensurability 31 9. Computability and Decidability 31 10. Concept, Term 31 11. Construction, Constructivity 34 12. Contradiction, Inconsistence, Antinomics 35 13. Copula 35 14. Counterfactuals, Fiction, see also : Modality 35 15. Decision 35 16. Deduction 36 17. Definition 36 18. Diagram, see also: Knowledge Representation 37 19. Dialectic 37 20. Dialethism, Dialetheism, see also: Contradiction and Paracon- sistent Logic 38 21. Discovery 38 22. Dogma 39 23. Entailment, Implication 39 24. Evidence 39 25. Falsity 40 26. Fallacy 40 27. Falsification 40 III 28. Family Resemblance 41 2 9. Formalism 41 3 0. Function 42 31. Functors, Junct ors, Logical Constants or Connectives 42 32. Holism 43 33. Hypothetical Propositions, Hypotheses 44 34. Idealiz ation 44 35. Id entity 44 36. Incommensurability 45 37. Incompleteness 45 38. -
Critical Thinking STARS Handout Finalx
Critical Thinking "It is the mark of an educated mind to be able to entertain a thought without accepting it." - Aristotle "The important thing is not to stop questioning." - Albert Einstein What is critical thinking? Critical thinking is the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action. In its exemplary form, it is based on universal intellectual values that transcend subject matter divisions: clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness. A Critical Thinker: Asks pertinent questions Assess statements and arguments Is able to admit a lack of understanding or information Has a sense of curiosity Is interested in finding new solutions Examines beliefs, assumptions and opinions and weighs them against facts Listens carefully to others and can give effective feedback Suspends judgment until all facts have been gathered and considered Look for evidence to support assumptions or beliefs Is able to adjust beliefs when new information is found Examines problems closely Is able to reject information that is irrelevant or incorrect Critical Thinking Standards and Questions: The most significant thinking (intellectual) standards/questions: • Clarity o Could you elaborate further on that point? o Could you give me an example? o Could you express -
Rules of Replacement II, §7.4
Philosophy 109, Modern Logic, Queens College Russell Marcus, Instructor email: [email protected] website: http://philosophy.thatmarcusfamily.org Office phone: (718) 997-5287 Rules of Replacement II, §7.4 I. The Last Five Rules of Replacement See the appendix at the end of the lesson for truth tables proving equivalence for each. Transposition (Trans) P e Q :: -Q e -P You may switch the antecedent and consequent of a conditional statement, as long as you negate (or un-negate) both. Often used with (HS). Also, traditionally, called the ‘contrapositive’. Sample Derivation: 1. A e B 2. D e -B / A e -D 3. --B e -D 2, Trans 4. A e -D 1, 3, DN, HS QED Transposition can be tricky when only one term is negated: A e -B becomes, by Trans: --B e -A which becomes, by DN B e -A Equivalently, but doing the double negation first: A e -B becomes, by DN: --A e -B becomes, by Trans: B e -A Either way, you can include the DN on the line with Trans. Material Implication (Impl) P e Q :: -P w Q Implication allows you to change a statement from a disjunction to a conditional, or vice versa. It’s often easier to work with disjunctions. You can use (DM) to get conjunctions. You may be able to use distribution, which doesn’t apply to conditionals. On the other hand, sometimes, you just want to work with conditionals. You can use (HS) and (MP). Proofs are overdetermined by our system - there are many ways to do them. -
Critical Thinking for the Military Professional
Document created: 17 Jun 04 Critical Thinking For The Military Professional Col W. Michael Guillot “Any complex activity, if it is to be carried on with any degree of virtuosity, calls for appropriate gifts of intellect and temperament …Genius consists in a harmonious combination of elements, in which one or the other ability may predominate, but none may be in conflict with the rest.”1 In a previous article on Strategic leadership I described the strategic environment as volatile, uncertain, complex, and ambiguous (VUCA). Additionally, that writing introduced the concept of strategic competency.2 This article will discuss the most important essential skill for Strategic Leaders: critical thinking. It is hard to imagine a Strategic leader today who does not think critically or at least uses the concept in making decisions. Critical thinking helps the strategic leader master the challenges of the strategic environment. It helps one understand how to bring stability to a volatile world. Critical thinking leads to more certainty and confidence in an uncertain future. This skill helps simplify complex scenarios and brings clarity to the ambiguous lens. Critical thinking is the kind of mental attitude required for success in the strategic environment. In essence, critical thinking is about learning how to think and how to judge and improve the quality of thinking—yours and others. Lest you feel you are already a great critical thinker, consider this, in a recent study supported by the Kellogg Foundation, only four percent of the U.S. organizational -
Recent Work in Relevant Logic
Recent Work in Relevant Logic Mark Jago Forthcoming in Analysis. Draft of April 2013. 1 Introduction Relevant logics are a group of logics which attempt to block irrelevant conclusions being drawn from a set of premises. The following inferences are all valid in classical logic, where A and B are any sentences whatsoever: • from A, one may infer B → A, B → B and B ∨ ¬B; • from ¬A, one may infer A → B; and • from A ∧ ¬A, one may infer B. But if A and B are utterly irrelevant to one another, many feel reluctant to call these inferences acceptable. Similarly for the validity of the corresponding material implications, often called ‘paradoxes’ of material implication. Relevant logic can be seen as the attempt to avoid these ‘paradoxes’. Relevant logic has a long history. Key early works include Anderson and Belnap 1962; 1963; 1975, and many important results appear in Routley et al. 1982. Those looking for a short introduction to relevant logics might look at Mares 2012 or Priest 2008. For a more detailed but still accessible introduction, there’s Dunn and Restall 2002; Mares 2004b; Priest 2008 and Read 1988. The aim of this article is to survey some of the most important work in the eld in the past ten years, in a way that I hope will be of interest to a philosophical audience. Much of this recent work has been of a formal nature. I will try to outline these technical developments, and convey something of their importance, with the minimum of technical jargon. A good deal of this recent technical work concerns how quantiers should work in relevant logic. -
7.1 Rules of Implication I
Natural Deduction is a method for deriving the conclusion of valid arguments expressed in the symbolism of propositional logic. The method consists of using sets of Rules of Inference (valid argument forms) to derive either a conclusion or a series of intermediate conclusions that link the premises of an argument with the stated conclusion. The First Four Rules of Inference: ◦ Modus Ponens (MP): p q p q ◦ Modus Tollens (MT): p q ~q ~p ◦ Pure Hypothetical Syllogism (HS): p q q r p r ◦ Disjunctive Syllogism (DS): p v q ~p q Common strategies for constructing a proof involving the first four rules: ◦ Always begin by attempting to find the conclusion in the premises. If the conclusion is not present in its entirely in the premises, look at the main operator of the conclusion. This will provide a clue as to how the conclusion should be derived. ◦ If the conclusion contains a letter that appears in the consequent of a conditional statement in the premises, consider obtaining that letter via modus ponens. ◦ If the conclusion contains a negated letter and that letter appears in the antecedent of a conditional statement in the premises, consider obtaining the negated letter via modus tollens. ◦ If the conclusion is a conditional statement, consider obtaining it via pure hypothetical syllogism. ◦ If the conclusion contains a letter that appears in a disjunctive statement in the premises, consider obtaining that letter via disjunctive syllogism. Four Additional Rules of Inference: ◦ Constructive Dilemma (CD): (p q) • (r s) p v r q v s ◦ Simplification (Simp): p • q p ◦ Conjunction (Conj): p q p • q ◦ Addition (Add): p p v q Common Misapplications Common strategies involving the additional rules of inference: ◦ If the conclusion contains a letter that appears in a conjunctive statement in the premises, consider obtaining that letter via simplification. -
Computational Advantages of Relevance Reasoning in Bayesian Belief Networks
342 Computational Advantages of Relevance Reasoning in Bayesian Belief Networks Yan Lin Marek J. Druzdzel University of Pittsburgh University of Pittsburgh Intelligent Systems Program Department of Information Science Pittsburgh, PA 1.5260 and Intelligent Systems Program [email protected] Pittsburgh, PA 15260 marek@sis. pitt. edu Abstract reasoning, shown to be NP-hard both for exact infer ence [Cooper, 1990] and for approximate [Dagurn. and This paper introduces a computational Luby, 1993] inference. framework for reasoning in Bayesian belief The critical factor in exact inference schemes is the networks that derives significant advantages topology of the underlying graph and, more specifi from focused inference and relevance reason cally, its connectivity. The complexity of approximate ing. This framework is based on d-separation schemes may, in addition, depend on factors like the and other simple and computationally effi a-priori likelihood of the observed evidenee or asymme cient techniques for pruning irrelevant parts tries in probability distributions. There are a number of a network. Our main contribution is a of ingeniously efficient algorithms that allow for fast technique that we call relevance-based decom belief updating in moderately sized models.2 Still, position. Relevance-based decomposition ap eaeh of them is subject to the growth in complexity proaches belief updating in large networks that is generally exponential in the size of the model. by focusing on their parts and decompos Given the promise of the decision-theoretic approach ing them into partially overlapping subnet and an increasing number of its practical applications, works. This makes reasoning in some in it is important to develop schemes that will reduce the tractable networks possible and, in addition, computational complexity of inference. -
Tables of Implications and Tautologies from Symbolic Logic
Tables of Implications and Tautologies from Symbolic Logic Dr. Robert B. Heckendorn Computer Science Department, University of Idaho March 17, 2021 Here are some tables of logical equivalents and implications that I have found useful over the years. Where there are classical names for things I have included them. \Isolation by Parts" is my own invention. By tautology I mean equivalent left and right hand side and by implication I mean the left hand expression implies the right hand. I use the tilde and overbar interchangeably to represent negation e.g. ∼x is the same as x. Enjoy! Table 1: Properties of All Two-bit Operators. The Comm. is short for commutative and Assoc. is short for associative. Iff is short for \if and only if". Truth Name Comm./ Binary And/Or/Not Nands Only Table Assoc. Op 0000 False CA 0 0 (a " (a " a)) " (a " (a " a)) 0001 And CA a ^ b a ^ b (a " b) " (a " b) 0010 Minus b − a a ^ b (b " (a " a)) " (a " (a " a)) 0011 B A b b b 0100 Minus a − b a ^ b (a " (a " a)) " (a " (a " b)) 0101 A A a a a 0110 Xor/NotEqual CA a ⊕ b (a ^ b) _ (a ^ b)(b " (a " a)) " (a " (a " b)) (a _ b) ^ (a _ b) 0111 Or CA a _ b a _ b (a " a) " (b " b) 1000 Nor C a # b a ^ b ((a " a) " (b " b)) " ((a " a) " a) 1001 Iff/Equal CA a $ b (a _ b) ^ (a _ b) ((a " a) " (b " b)) " (a " b) (a ^ b) _ (a ^ b) 1010 Not A a a a " a 1011 Imply a ! b a _ b (a " (a " b)) 1100 Not B b b b " b 1101 Imply b ! a a _ b (b " (a " a)) 1110 Nand C a " b a _ b a " b 1111 True CA 1 1 (a " a) " a 1 Table 2: Tautologies (Logical Identities) Commutative Property: p ^ q $ q -
Relevant and Substructural Logics
Relevant and Substructural Logics GREG RESTALL∗ PHILOSOPHY DEPARTMENT, MACQUARIE UNIVERSITY [email protected] June 23, 2001 http://www.phil.mq.edu.au/staff/grestall/ Abstract: This is a history of relevant and substructural logics, written for the Hand- book of the History and Philosophy of Logic, edited by Dov Gabbay and John Woods.1 1 Introduction Logics tend to be viewed of in one of two ways — with an eye to proofs, or with an eye to models.2 Relevant and substructural logics are no different: you can focus on notions of proof, inference rules and structural features of deduction in these logics, or you can focus on interpretations of the language in other structures. This essay is structured around the bifurcation between proofs and mod- els: The first section discusses Proof Theory of relevant and substructural log- ics, and the second covers the Model Theory of these logics. This order is a natural one for a history of relevant and substructural logics, because much of the initial work — especially in the Anderson–Belnap tradition of relevant logics — started by developing proof theory. The model theory of relevant logic came some time later. As we will see, Dunn's algebraic models [76, 77] Urquhart's operational semantics [267, 268] and Routley and Meyer's rela- tional semantics [239, 240, 241] arrived decades after the initial burst of ac- tivity from Alan Anderson and Nuel Belnap. The same goes for work on the Lambek calculus: although inspired by a very particular application in lin- guistic typing, it was developed first proof-theoretically, and only later did model theory come to the fore. -
From Axioms to Rules — a Coalition of Fuzzy, Linear and Substructural Logics
From Axioms to Rules — A Coalition of Fuzzy, Linear and Substructural Logics Kazushige Terui National Institute of Informatics, Tokyo Laboratoire d’Informatique de Paris Nord (Joint work with Agata Ciabattoni and Nikolaos Galatos) Genova, 21/02/08 – p.1/?? Parties in Nonclassical Logics Modal Logics Default Logic Intermediate Logics (Padova) Basic Logic Paraconsistent Logic Linear Logic Fuzzy Logics Substructural Logics Genova, 21/02/08 – p.2/?? Parties in Nonclassical Logics Modal Logics Default Logic Intermediate Logics (Padova) Basic Logic Paraconsistent Logic Linear Logic Fuzzy Logics Substructural Logics Our aim: Fruitful coalition of the 3 parties Genova, 21/02/08 – p.2/?? Basic Requirements Substractural Logics: Algebraization ´µ Ä Î ´Äµ Genova, 21/02/08 – p.3/?? Basic Requirements Substractural Logics: Algebraization ´µ Ä Î ´Äµ Fuzzy Logics: Standard Completeness ´µ Ä Ã ´Äµ ¼½ Genova, 21/02/08 – p.3/?? Basic Requirements Substractural Logics: Algebraization ´µ Ä Î ´Äµ Fuzzy Logics: Standard Completeness ´µ Ä Ã ´Äµ ¼½ Linear Logic: Cut Elimination Genova, 21/02/08 – p.3/?? Basic Requirements Substractural Logics: Algebraization ´µ Ä Î ´Äµ Fuzzy Logics: Standard Completeness ´µ Ä Ã ´Äµ ¼½ Linear Logic: Cut Elimination A logic without cut elimination is like a car without engine (J.-Y. Girard) Genova, 21/02/08 – p.3/?? Outcome We classify axioms in Substructural and Fuzzy Logics according to the Substructural Hierarchy, which is defined based on Polarity (Linear Logic). Genova, 21/02/08 – p.4/?? Outcome We classify axioms in Substructural and Fuzzy Logics according to the Substructural Hierarchy, which is defined based on Polarity (Linear Logic). Give an automatic procedure to transform axioms up to level ¼ È È ¿ ( , in the absense of Weakening) into Hyperstructural ¿ Rules in Hypersequent Calculus (Fuzzy Logics).