Methods of Proofs
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Chapter 5: Methods of Proof for Boolean Logic
Chapter 5: Methods of Proof for Boolean Logic § 5.1 Valid inference steps Conjunction elimination Sometimes called simplification. From a conjunction, infer any of the conjuncts. • From P ∧ Q, infer P (or infer Q). Conjunction introduction Sometimes called conjunction. From a pair of sentences, infer their conjunction. • From P and Q, infer P ∧ Q. § 5.2 Proof by cases This is another valid inference step (it will form the rule of disjunction elimination in our formal deductive system and in Fitch), but it is also a powerful proof strategy. In a proof by cases, one begins with a disjunction (as a premise, or as an intermediate conclusion already proved). One then shows that a certain consequence may be deduced from each of the disjuncts taken separately. One concludes that that same sentence is a consequence of the entire disjunction. • From P ∨ Q, and from the fact that S follows from P and S also follows from Q, infer S. The general proof strategy looks like this: if you have a disjunction, then you know that at least one of the disjuncts is true—you just don’t know which one. So you consider the individual “cases” (i.e., disjuncts), one at a time. You assume the first disjunct, and then derive your conclusion from it. You repeat this process for each disjunct. So it doesn’t matter which disjunct is true—you get the same conclusion in any case. Hence you may infer that it follows from the entire disjunction. In practice, this method of proof requires the use of “subproofs”—we will take these up in the next chapter when we look at formal proofs. -
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. -
Predicate Logic. Formal and Informal Proofs
CS 441 Discrete Mathematics for CS Lecture 5 Predicate logic Milos Hauskrecht [email protected] 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Negation of quantifiers English statement: • Nothing is perfect. • Translation: ¬ x Perfect(x) Another way to express the same meaning: • Everything ... M. Hauskrecht 1 Negation of quantifiers English statement: • Nothing is perfect. • Translation: ¬ x Perfect(x) Another way to express the same meaning: • Everything is imperfect. • Translation: x ¬ Perfect(x) Conclusion: ¬ x P (x) is equivalent to x ¬ P(x) M. Hauskrecht Negation of quantifiers English statement: • It is not the case that all dogs are fleabags. • Translation: ¬ x Dog(x) Fleabag(x) Another way to express the same meaning: • There is a dog that … M. Hauskrecht 2 Negation of quantifiers English statement: • It is not the case that all dogs are fleabags. • Translation: ¬ x Dog(x) Fleabag(x) Another way to express the same meaning: • There is a dog that is not a fleabag. • Translation: x Dog(x) ¬ Fleabag(x) • Logically equivalent to: – x ¬ ( Dog(x) Fleabag(x) ) Conclusion: ¬ x P (x) is equivalent to x ¬ P(x) M. Hauskrecht Negation of quantified statements (aka DeMorgan Laws for quantifiers) Negation Equivalent ¬x P(x) x ¬P(x) ¬x P(x) x ¬P(x) M. Hauskrecht 3 Formal and informal proofs CS 441 Discrete mathematics for CS M. Hauskrecht Theorems and proofs • The truth value of some statement about the world is obvious and easy to assign • The truth of other statements may not be obvious, … …. But it may still follow (be derived) from known facts about the world To show the truth value of such a statement following from other statements we need to provide a correct supporting argument - a proof Important questions: – When is the argument correct? – How to construct a correct argument, what method to use? CS 441 Discrete mathematics for CS M. -
Notes on Proof Theory
Notes on Proof Theory Master 1 “Informatique”, Univ. Paris 13 Master 2 “Logique Mathématique et Fondements de l’Informatique”, Univ. Paris 7 Damiano Mazza November 2016 1Last edit: March 29, 2021 Contents 1 Propositional Classical Logic 5 1.1 Formulas and truth semantics . 5 1.2 Atomic negation . 8 2 Sequent Calculus 10 2.1 Two-sided formulation . 10 2.2 One-sided formulation . 13 3 First-order Quantification 16 3.1 Formulas and truth semantics . 16 3.2 Sequent calculus . 19 3.3 Ultrafilters . 21 4 Completeness 24 4.1 Exhaustive search . 25 4.2 The completeness proof . 30 5 Undecidability and Incompleteness 33 5.1 Informal computability . 33 5.2 Incompleteness: a road map . 35 5.3 Logical theories . 38 5.4 Arithmetical theories . 40 5.5 The incompleteness theorems . 44 6 Cut Elimination 47 7 Intuitionistic Logic 53 7.1 Sequent calculus . 55 7.2 The relationship between intuitionistic and classical logic . 60 7.3 Minimal logic . 65 8 Natural Deduction 67 8.1 Sequent presentation . 68 8.2 Natural deduction and sequent calculus . 70 8.3 Proof tree presentation . 73 8.3.1 Minimal natural deduction . 73 8.3.2 Intuitionistic natural deduction . 75 1 8.3.3 Classical natural deduction . 75 8.4 Normalization (cut-elimination in natural deduction) . 76 9 The Curry-Howard Correspondence 80 9.1 The simply typed l-calculus . 80 9.2 Product and sum types . 81 10 System F 83 10.1 Intuitionistic second-order propositional logic . 83 10.2 Polymorphic types . 84 10.3 Programming in system F ...................... 85 10.3.1 Free structures . -
Classical First-Order Logic Software Formal Verification
Classical First-Order Logic Software Formal Verification Maria Jo~aoFrade Departmento de Inform´atica Universidade do Minho 2008/2009 Maria Jo~aoFrade (DI-UM) First-Order Logic (Classical) MFES 2008/09 1 / 31 Introduction First-order logic (FOL) is a richer language than propositional logic. Its lexicon contains not only the symbols ^, _, :, and ! (and parentheses) from propositional logic, but also the symbols 9 and 8 for \there exists" and \for all", along with various symbols to represent variables, constants, functions, and relations. There are two sorts of things involved in a first-order logic formula: terms, which denote the objects that we are talking about; formulas, which denote truth values. Examples: \Not all birds can fly." \Every child is younger than its mother." \Andy and Paul have the same maternal grandmother." Maria Jo~aoFrade (DI-UM) First-Order Logic (Classical) MFES 2008/09 2 / 31 Syntax Variables: x; y; z; : : : 2 X (represent arbitrary elements of an underlying set) Constants: a; b; c; : : : 2 C (represent specific elements of an underlying set) Functions: f; g; h; : : : 2 F (every function f as a fixed arity, ar(f)) Predicates: P; Q; R; : : : 2 P (every predicate P as a fixed arity, ar(P )) Fixed logical symbols: >, ?, ^, _, :, 8, 9 Fixed predicate symbol: = for \equals" (“first-order logic with equality") Maria Jo~aoFrade (DI-UM) First-Order Logic (Classical) MFES 2008/09 3 / 31 Syntax Terms The set T , of terms of FOL, is given by the abstract syntax T 3 t ::= x j c j f(t1; : : : ; tar(f)) Formulas The set L, of formulas of FOL, is given by the abstract syntax L 3 φ, ::= ? j > j :φ j φ ^ j φ _ j φ ! j t1 = t2 j 8x: φ j 9x: φ j P (t1; : : : ; tar(P )) :, 8, 9 bind most tightly; then _ and ^; then !, which is right-associative. -
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. -
Qvp) P :: ~~Pp :: (Pvp) ~(P → Q
TEN BASIC RULES OF INFERENCE Negation Introduction (~I – indirect proof IP) Disjunction Introduction (vI – addition ADD) Assume p p Get q & ~q ˫ p v q ˫ ~p Disjunction Elimination (vE – version of CD) Negation Elimination (~E – version of DN) p v q ~~p → p p → r Conditional Introduction (→I – conditional proof CP) q → r Assume p ˫ r Get q Biconditional Introduction (↔I – version of ME) ˫ p → q p → q Conditional Elimination (→E – modus ponens MP) q → p p → q ˫ p ↔ q p Biconditional Elimination (↔E – version of ME) ˫ q p ↔ q Conjunction Introduction (&I – conjunction CONJ) ˫ p → q p or q ˫ q → p ˫ p & q Conjunction Elimination (&E – simplification SIMP) p & q ˫ p IMPORTANT DERIVED RULES OF INFERENCE Modus Tollens (MT) Constructive Dilemma (CD) p → q p v q ~q p → r ˫ ~P q → s Hypothetical Syllogism (HS) ˫ r v s p → q Repeat (RE) q → r p ˫ p → r ˫ p Disjunctive Syllogism (DS) Contradiction (CON) p v q p ~p ~p ˫ q ˫ Any wff Absorption (ABS) Theorem Introduction (TI) p → q Introduce any tautology, e.g., ~(P & ~P) ˫ p → (p & q) EQUIVALENCES De Morgan’s Law (DM) (p → q) :: (~q→~p) ~(p & q) :: (~p v ~q) Material implication (MI) ~(p v q) :: (~p & ~q) (p → q) :: (~p v q) Commutation (COM) Material Equivalence (ME) (p v q) :: (q v p) (p ↔ q) :: [(p & q ) v (~p & ~q)] (p & q) :: (q & p) (p ↔ q) :: [(p → q ) & (q → p)] Association (ASSOC) Exportation (EXP) [p v (q v r)] :: [(p v q) v r] [(p & q) → r] :: [p → (q → r)] [p & (q & r)] :: [(p & q) & r] Tautology (TAUT) Distribution (DIST) p :: (p & p) [p & (q v r)] :: [(p & q) v (p & r)] p :: (p v p) [p v (q & r)] :: [(p v q) & (p v r)] Conditional-Biconditional Refutation Tree Rules Double Negation (DN) ~(p → q) :: (p & ~q) p :: ~~p ~(p ↔ q) :: [(p & ~q) v (~p & q)] Transposition (TRANS) CATEGORICAL SYLLOGISM RULES (e.g., Ǝx(Fx) / ˫ Fy). -
Natural Deduction with Propositional Logic
Natural Deduction with Propositional Logic Introducing Natural Natural Deduction with Propositional Logic Deduction Ling 130: Formal Semantics Some basic rules without assumptions Rules with assumptions Spring 2018 Outline Natural Deduction with Propositional Logic Introducing 1 Introducing Natural Deduction Natural Deduction Some basic rules without assumptions 2 Some basic rules without assumptions Rules with assumptions 3 Rules with assumptions What is ND and what's so natural about it? Natural Deduction with Natural Deduction Propositional Logic A system of logical proofs in which assumptions are freely introduced but discharged under some conditions. Introducing Natural Deduction Introduced independently and simultaneously (1934) by Some basic Gerhard Gentzen and Stanis law Ja´skowski rules without assumptions Rules with assumptions The book & slides/handouts/HW represent two styles of one ND system: there are several. Introduced originally to capture the style of reasoning used by mathematicians in their proofs. Ancient antecedents Natural Deduction with Propositional Logic Aristotle's syllogistics can be interpreted in terms of inference rules and proofs from assumptions. Introducing Natural Deduction Some basic rules without assumptions Rules with Stoic logic includes a practical application of a ND assumptions theorem. ND rules and proofs Natural Deduction with Propositional There are at least two rules for each connective: Logic an introduction rule an elimination rule Introducing Natural The rules reflect the meanings (e.g. as represented by Deduction Some basic truth-tables) of the connectives. rules without assumptions Rules with Parts of each ND proof assumptions You should have four parts to each line of your ND proof: line number, the formula, justification for writing down that formula, the goal for that part of the proof. -
Chapter 10: Symbolic Trails and Formal Proofs of Validity, Part 2
Essential Logic Ronald C. Pine CHAPTER 10: SYMBOLIC TRAILS AND FORMAL PROOFS OF VALIDITY, PART 2 Introduction In the previous chapter there were many frustrating signs that something was wrong with our formal proof method that relied on only nine elementary rules of validity. Very simple, intuitive valid arguments could not be shown to be valid. For instance, the following intuitively valid arguments cannot be shown to be valid using only the nine rules. Somalia and Iran are both foreign policy risks. Therefore, Iran is a foreign policy risk. S I / I Either Obama or McCain was President of the United States in 2009.1 McCain was not President in 2010. So, Obama was President of the United States in 2010. (O v C) ~(O C) ~C / O If the computer networking system works, then Johnson and Kaneshiro will both be connected to the home office. Therefore, if the networking system works, Johnson will be connected to the home office. N (J K) / N J Either the Start II treaty is ratified or this landmark treaty will not be worth the paper it is written on. Therefore, if the Start II treaty is not ratified, this landmark treaty will not be worth the paper it is written on. R v ~W / ~R ~W 1 This or statement is obviously exclusive, so note the translation. 427 If the light is on, then the light switch must be on. So, if the light switch in not on, then the light is not on. L S / ~S ~L Thus, the nine elementary rules of validity covered in the previous chapter must be only part of a complete system for constructing formal proofs of validity. -
Argument Forms and Fallacies
6.6 Common Argument Forms and Fallacies 1. Common Valid Argument Forms: In the previous section (6.4), we learned how to determine whether or not an argument is valid using truth tables. There are certain forms of valid and invalid argument that are extremely common. If we memorize some of these common argument forms, it will save us time because we will be able to immediately recognize whether or not certain arguments are valid or invalid without having to draw out a truth table. Let’s begin: 1. Disjunctive Syllogism: The following argument is valid: “The coin is either in my right hand or my left hand. It’s not in my right hand. So, it must be in my left hand.” Let “R”=”The coin is in my right hand” and let “L”=”The coin is in my left hand”. The argument in symbolic form is this: R ˅ L ~R __________________________________________________ L Any argument with the form just stated is valid. This form of argument is called a disjunctive syllogism. Basically, the argument gives you two options and says that, since one option is FALSE, the other option must be TRUE. 2. Pure Hypothetical Syllogism: The following argument is valid: “If you hit the ball in on this turn, you’ll get a hole in one; and if you get a hole in one you’ll win the game. So, if you hit the ball in on this turn, you’ll win the game.” Let “B”=”You hit the ball in on this turn”, “H”=”You get a hole in one”, and “W”=”you win the game”. -
Philosophy 109, Modern Logic Russell Marcus
Philosophy 240: Symbolic Logic Hamilton College Fall 2014 Russell Marcus Reference Sheeet for What Follows Names of Languages PL: Propositional Logic M: Monadic (First-Order) Predicate Logic F: Full (First-Order) Predicate Logic FF: Full (First-Order) Predicate Logic with functors S: Second-Order Predicate Logic Basic Truth Tables - á á @ â á w â á e â á / â 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 Rules of Inference Modus Ponens (MP) Conjunction (Conj) á e â á á / â â / á A â Modus Tollens (MT) Addition (Add) á e â á / á w â -â / -á Simplification (Simp) Disjunctive Syllogism (DS) á A â / á á w â -á / â Constructive Dilemma (CD) (á e â) Hypothetical Syllogism (HS) (ã e ä) á e â á w ã / â w ä â e ã / á e ã Philosophy 240: Symbolic Logic, Prof. Marcus; Reference Sheet for What Follows, page 2 Rules of Equivalence DeMorgan’s Laws (DM) Contraposition (Cont) -(á A â) W -á w -â á e â W -â e -á -(á w â) W -á A -â Material Implication (Impl) Association (Assoc) á e â W -á w â á w (â w ã) W (á w â) w ã á A (â A ã) W (á A â) A ã Material Equivalence (Equiv) á / â W (á e â) A (â e á) Distribution (Dist) á / â W (á A â) w (-á A -â) á A (â w ã) W (á A â) w (á A ã) á w (â A ã) W (á w â) A (á w ã) Exportation (Exp) á e (â e ã) W (á A â) e ã Commutativity (Com) á w â W â w á Tautology (Taut) á A â W â A á á W á A á á W á w á Double Negation (DN) á W --á Six Derived Rules for the Biconditional Rules of Inference Rules of Equivalence Biconditional Modus Ponens (BMP) Biconditional DeMorgan’s Law (BDM) á / â -(á / â) W -á / â á / â Biconditional Modus Tollens (BMT) Biconditional Commutativity (BCom) á / â á / â W â / á -á / -â Biconditional Hypothetical Syllogism (BHS) Biconditional Contraposition (BCont) á / â á / â W -á / -â â / ã / á / ã Philosophy 240: Symbolic Logic, Prof. -
Logic and Proof
Logic and Proof Computer Science Tripos Part IB Michaelmas Term Lawrence C Paulson Computer Laboratory University of Cambridge [email protected] Copyright c 2000 by Lawrence C. Paulson Contents 1 Introduction and Learning Guide 1 2 Propositional Logic 3 3 Proof Systems for Propositional Logic 12 4 Ordered Binary Decision Diagrams 19 5 First-order Logic 22 6 Formal Reasoning in First-Order Logic 29 7 Clause Methods for Propositional Logic 34 8 Skolem Functions and Herbrand’s Theorem 42 9 Unification 49 10 Applications of Unification 58 11 Modal Logics 65 12 Tableaux-Based Methods 70 i ii 1 1 Introduction and Learning Guide This course gives a brief introduction to logic, with including the resolution method of theorem-proving and its relation to the programming language Prolog. Formal logic is used for specifying and verifying computer systems and (some- times) for representing knowledge in Artificial Intelligence programs. The course should help you with Prolog for AI and its treatment of logic should be helpful for understanding other theoretical courses. Try to avoid getting bogged down in the details of how the various proof methods work, since you must also acquire an intuitive feel for logical reasoning. The most suitable course text is this book: Michael Huth and Mark Ryan, Logic in Computer Science: Modelling and Reasoning about Systems (CUP, 2000) It costs £18.36 from Amazon. It covers most aspects of this course with the ex- ception of resolution theorem proving. It includes material that may be useful in Specification and Verification II next year, namely symbolic model checking.