Introduction to Logic 1

Total Page:16

File Type:pdf, Size:1020Kb

Introduction to Logic 1 Introduction to logic 1. Logic and Artificial Intelligence: some historical remarks One of the aims of AI is to reproduce human features by means of a computer system. One of these features is reasoning. Reasoning can be viewed as the process of having a knowledge base (KB) and manipulating it to create new knowledge. Reasoning can be seen as comprised of 3 processes: 1. Perceiving stimuli from the environment; 2. Translating the stimuli in components of the KB; 3. Working on the KB to increase it. We are going to deal with how to represent information in the KB and how to reason about it. We use logic as a device to pursue this aim. These notes are an introduction to modern logic, whose origin can be found in George Boole’s and Gottlob Frege’s works in the XIX century. However, logic in general traces back to ancient Greek philosophers that studied under which conditions an argument is valid. An argument is any set of statements – explicit or implicit – one of which is the conclusion (the statement being defended) and the others are the premises (the statements providing the defense). The relationship between the conclusion and the premises is such that the conclusion follows from the premises (Lepore 2003). Modern logic is a powerful tool to represent and reason on knowledge and AI has traditionally adopted it as working tool. Within logic, one research tradition that strongly influenced AI is that started by the German philosopher and mathematician Gottfried Leibniz. His aim was to formulate a Lingua Rationalis to create a universal language based only on rationality, which could solve all the controversies between different parties. This language was supposed to be comprised of a: - characteristica universalis: a set of symbols with which one may express all the sentences of this language; and a - calculus ratiocinator: a set of rules by which one may deduce all possible truths from an initial list of thoughts (expressed in the form of a characteristica universalis sentence). Leibniz’s very ambitious project failed: he encountered several difficulties already in the development of the characteristica universalis due to the absence of an appropriate formalism for this kind of language. The necessary steps to the development of logic in its modern form were taken by George Boole (1854) and Gottlob Frege (1879). Boole revolutionized logic by applying methods from the then- emerging field of symbolic algebra to logic. Where traditional (Aristotelian) logic relied on cataloging the valid syllogisms of various simple forms, Boole's method provided general algorithms in an algebraic language which applied to an infinite variety of arguments of arbitrary complexity. Frege essentially reconceived the discipline of logic by constructing a formal system which, in effect, constituted the first ‘predicate calculus’. In this formal system, Frege developed an analysis of quantified statements and formalized the notion of a ‘proof’ in terms that are still accepted today. Frege then demonstrated that one could use his system to resolve theoretical mathematical statements in terms of simpler logical and mathematical notions. There are different types of logic, which we can use to pursue our aim of representing and reasoning on knowledge, but we start with the simplest one, called Propositional Logic (PL). Then we present a 2 more powerful type of logic, called First Order Logic (FOL) or Predicate Calculus, which is the one usually adopted in AI. 2. Introduction to key terms Before presenting PL and FOL, we briefly introduce some key terms of logic (argument, deductive validity, soundness) (Lepore 2003) that, in the following, we will decline within the specific logical languages we will present. 2.1 Arguments As said, an argument is any set of statements – explicit or implicit – one of which is the conclusion (the statement being defended) and the others are the premises (statements providing the defense). The relationship between the conclusion and the premises is such that the conclusion follows from the premises. Here is one example. (2.1) Anyone who deliberates about alternative courses of action believes he is free. Since everybody deliberates about alternative courses of action, it follows that we all believe ourselves to be free. A statement is any indicative sentence that is either true or false. ‘Galileo was an astronomer’ is a statement; ‘Is George Washington president?’ is not a statement. Putting arguments into a standard format Having determined that some part of discourse contains an argument, the next task is to put it into a standard format. As said, an argument is any set of statements – explicit or implicit – one of which is the conclusion, and the others are the premises. To put an argument in a standard form requires all the following steps: - to identify the premises and the conclusion; - to place the premises first and the conclusion last; - to make explicit any premise or even the conclusion; this may be only implicit in the argument, but essential to the argument itself. So the argument presented as example above (2.1) has the following standard format. Premise 1: Anyone who deliberates about alternative courses of action believes he is free. Premise 2: Everybody deliberates about alternative courses of action. Conclusion: We all believe ourselves to be free. To make statements (both premises and conclusions) explicit is often critical when putting an argument into standard format. Let us consider another example in which both one of the two premises and the conclusion are hidden in its natural format. (2.2) What’s the problem today with John? Everybody is afraid to go to the dentist. Premise 1: Everybody is afraid to go to the dentist. Premise 2 (hidden): John is going to the dentist today. Conclusion (hidden): John is afraid. 2.2 Deductive validity Defining an argument, we have said that the relationship between the conclusion and the premises is such that the conclusion purportedly follows from the premises. But what is for one statement to ‘follow from others’? The principal sense of ‘follows from’ derives from the notion of deductively valid argument. 3 A deductively valid argument is an argument such that it is not possible both for its premises to be true and its conclusion to be false. In other terms, in a valid argument it is not possible that, if the premises are true, the conclusion is false. (2.3) All men are mortal. Socrates is a man. So Socrates is mortal. (2.3) is a deductively valid argument, whose conclusion follows from its premises. (2.4) All bachelors are nice. John is a bachelor. So John is nice. (2.4) is also a deductively valid argument. However, its premises should be false. It is worth noting that to determine whether a specific statement is true or false is not the aim of logic. Logic cannot say if the statements ‘All bachelors are nice’ and ‘John is a bachelor’ are true or false. This has to be determined by means of empirical analysis. The aim of logic, rather, is to determine if the argument is valid: given that the premises are true, the conclusion has to be true as well. If in ordinary language the terms ‘valid’ and ‘true’ are often used interchangeably, in logic ‘valid’ and ‘invalid’ apply only to arguments, while ‘true’ and ‘false’ only to statements. 2.3 Soundness An argument can be deductively valid, but unlikely to persuade anyone. Normally good arguments are not only deductively valid. They also have true premises. These arguments are called sound. Argument (2.4) above is valid, but not sound. Another example: (2.5) All fish fly. Anything which flies talks. So, all fish talk. This is a deductively valid argument but unlikely to persuade anyone. 3. Propositional Logic In learning Propositional Logic (PL) we learn ways of representing arguments correctly and ways of testing the validity of those arguments. There exist various techniques to determine whether arguments containing these types of statements are deductively valid in virtue of their form and PL is one of them. PL, like any other logic, can be thought as comprised of three components: - syntax: which specifies how to build sentences of the logic; - semantics: which attaches to these sentences a meaning; - inference rules: which manipulate these sentences to obtain more sentences. The basic symbolization rule in PL is that a simple statement is symbolized by a single statement letter. 3.1 Syntax of PL It defines the allowable sentences in PL. PL sentences are comprised of: Atomic sentences: indivisible syntactic elements consisting of a single propositional symbol, usually an upper case letter (P, Q, R). Every symbol represents a sentence that can be true (T) or false (F). Complex sentences: constructed from atomic ones by means of logical connectives, ¬ (negation), ∧ (conjunction), ∨ (disjunction), → (conditional), ↔ (biconditional). 4 Let us analyze in more details logical connectives: Negation ¬ - if P is a PL sentence, ¬P is a PL sentence, too. - ¬P is called the negation of P. - If P is an atomic sentence, both P and ¬P are called literals (positive and negative respectively). Conjunction ∧ - If P and Q are PL sentences, P ∧ Q is a PL sentence. Disjunction ∨ - If P and Q are PL sentences, P ∨ Q is a PL sentence. Conditional → - If P and Q are PL sentences, P → Q is a PL sentence. Biconditional ↔ - If P and Q are PL sentences, P ↔ Q is a PL sentence. 3.2 Semantics of PL The semantics defines the rules for determining the truth of a sentence with respect to a particular model. A model fixes the truth value (True, False) for every proposition symbol. A model is a pure mathematical object, with no necessary connection to the actual world.
Recommended publications
  • 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.
    [Show full text]
  • UNIT-I Mathematical Logic Statements and Notations
    UNIT-I Mathematical Logic Statements and notations: A proposition or statement is a declarative sentence that is either true or false (but not both). For instance, the following are propositions: “Paris is in France” (true), “London is in Denmark” (false), “2 < 4” (true), “4 = 7 (false)”. However the following are not propositions: “what is your name?” (this is a question), “do your homework” (this is a command), “this sentence is false” (neither true nor false), “x is an even number” (it depends on what x represents), “Socrates” (it is not even a sentence). The truth or falsehood of a proposition is called its truth value. Connectives: Connectives are used for making compound propositions. The main ones are the following (p and q represent given propositions): Name Represented Meaning Negation ¬p “not p” Conjunction p q “p and q” Disjunction p q “p or q (or both)” Exclusive Or p q “either p or q, but not both” Implication p ⊕ q “if p then q” Biconditional p q “p if and only if q” Truth Tables: Logical identity Logical identity is an operation on one logical value, typically the value of a proposition that produces a value of true if its operand is true and a value of false if its operand is false. The truth table for the logical identity operator is as follows: Logical Identity p p T T F F Logical negation Logical negation is an operation on one logical value, typically the value of a proposition that produces a value of true if its operand is false and a value of false if its operand is true.
    [Show full text]
  • Chapter 1 Logic and Set Theory
    Chapter 1 Logic and Set Theory To criticize mathematics for its abstraction is to miss the point entirely. Abstraction is what makes mathematics work. If you concentrate too closely on too limited an application of a mathematical idea, you rob the mathematician of his most important tools: analogy, generality, and simplicity. – Ian Stewart Does God play dice? The mathematics of chaos In mathematics, a proof is a demonstration that, assuming certain axioms, some statement is necessarily true. That is, a proof is a logical argument, not an empir- ical one. One must demonstrate that a proposition is true in all cases before it is considered a theorem of mathematics. An unproven proposition for which there is some sort of empirical evidence is known as a conjecture. Mathematical logic is the framework upon which rigorous proofs are built. It is the study of the principles and criteria of valid inference and demonstrations. Logicians have analyzed set theory in great details, formulating a collection of axioms that affords a broad enough and strong enough foundation to mathematical reasoning. The standard form of axiomatic set theory is denoted ZFC and it consists of the Zermelo-Fraenkel (ZF) axioms combined with the axiom of choice (C). Each of the axioms included in this theory expresses a property of sets that is widely accepted by mathematicians. It is unfortunately true that careless use of set theory can lead to contradictions. Avoiding such contradictions was one of the original motivations for the axiomatization of set theory. 1 2 CHAPTER 1. LOGIC AND SET THEORY A rigorous analysis of set theory belongs to the foundations of mathematics and mathematical logic.
    [Show full text]
  • Folktale Documentation Release 1.0
    Folktale Documentation Release 1.0 Quildreen Motta Nov 05, 2017 Contents 1 Guides 3 2 Indices and tables 5 3 Other resources 7 3.1 Getting started..............................................7 3.2 Folktale by Example........................................... 10 3.3 API Reference.............................................. 12 3.4 How do I................................................... 63 3.5 Glossary................................................. 63 Python Module Index 65 i ii Folktale Documentation, Release 1.0 Folktale is a suite of libraries for generic functional programming in JavaScript that allows you to write elegant modular applications with fewer bugs, and more reuse. Contents 1 Folktale Documentation, Release 1.0 2 Contents CHAPTER 1 Guides • Getting Started A series of quick tutorials to get you up and running quickly with the Folktale libraries. • API reference A quick reference of Folktale’s libraries, including usage examples and cross-references. 3 Folktale Documentation, Release 1.0 4 Chapter 1. Guides CHAPTER 2 Indices and tables • Global Module Index Quick access to all modules. • General Index All functions, classes, terms, sections. • Search page Search this documentation. 5 Folktale Documentation, Release 1.0 6 Chapter 2. Indices and tables CHAPTER 3 Other resources • Licence information 3.1 Getting started This guide will cover everything you need to start using the Folktale project right away, from giving you a brief overview of the project, to installing it, to creating a simple example. Once you get the hang of things, the Folktale By Example guide should help you understanding the concepts behind the library, and mapping them to real use cases. 3.1.1 So, what’s Folktale anyways? Folktale is a suite of libraries for allowing a particular style of functional programming in JavaScript.
    [Show full text]
  • Theorem Proving in Classical Logic
    MEng Individual Project Imperial College London Department of Computing Theorem Proving in Classical Logic Supervisor: Dr. Steffen van Bakel Author: David Davies Second Marker: Dr. Nicolas Wu June 16, 2021 Abstract It is well known that functional programming and logic are deeply intertwined. This has led to many systems capable of expressing both propositional and first order logic, that also operate as well-typed programs. What currently ties popular theorem provers together is their basis in intuitionistic logic, where one cannot prove the law of the excluded middle, ‘A A’ – that any proposition is either true or false. In classical logic this notion is provable, and the_: corresponding programs turn out to be those with control operators. In this report, we explore and expand upon the research about calculi that correspond with classical logic; and the problems that occur for those relating to first order logic. To see how these calculi behave in practice, we develop and implement functional languages for propositional and first order logic, expressing classical calculi in the setting of a theorem prover, much like Agda and Coq. In the first order language, users are able to define inductive data and record types; importantly, they are able to write computable programs that have a correspondence with classical propositions. Acknowledgements I would like to thank Steffen van Bakel, my supervisor, for his support throughout this project and helping find a topic of study based on my interests, for which I am incredibly grateful. His insight and advice have been invaluable. I would also like to thank my second marker, Nicolas Wu, for introducing me to the world of dependent types, and suggesting useful resources that have aided me greatly during this report.
    [Show full text]
  • Learning Dependency-Based Compositional Semantics
    Learning Dependency-Based Compositional Semantics Percy Liang∗ University of California, Berkeley Michael I. Jordan∗∗ University of California, Berkeley Dan Klein† University of California, Berkeley Suppose we want to build a system that answers a natural language question by representing its semantics as a logical form and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive. Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms. 1. Introduction One of the major challenges in natural language processing (NLP) is building systems that both handle complex linguistic phenomena and require minimal human effort. The difficulty of achieving both criteria is particularly evident in training semantic parsers, where annotating linguistic expressions with their associated logical forms is expensive but until recently, seemingly unavoidable. Advances in learning latent-variable models, however, have made it possible to progressively reduce the amount of supervision ∗ Computer Science Division, University of California, Berkeley, CA 94720, USA.
    [Show full text]
  • Propositional Logic
    Chapter 3 Propositional Logic 3.1 ARGUMENT FORMS This chapter begins our treatment of formal logic. Formal logic is the study of argument forms, abstract patterns of reasoning shared by many different arguments. The study of argument forms facilitates broad and illuminating generalizations about validity and related topics. We shall initially focus on the notion of deductive validity, leaving inductive arguments to a later treatment (Chapters 8 to 10). Specifically, our concern in this chapter will be with the idea that a valid deductive argument is one whose conclusion cannot be false while the premises are all true (see Section 2.3). By studying argument forms, we shall be able to give this idea a very precise and rigorous characterization. We begin with three arguments which all have the same form: (1) Today is either Monday or Tuesday. Today is not Monday. Today is Tuesday. (2) Either Rembrandt painted the Mona Lisa or Michelangelo did. Rembrandt didn't do it. :. Michelangelo did. (3) Either he's at least 18 or he's a juvenile. He's not at least 18. :. He's a juvenile. It is easy to see that these three arguments are all deductively valid. Their common form is known by logicians as disjunctive syllogism, and can be represented as follows: Either P or Q. It is not the case that P :. Q. The letters 'P' and 'Q' function here as placeholders for declarative' sentences. We shall call such letters sentence letters. Each argument which has this form is obtainable from the form by replacing the sentence letters with sentences, each occurrence of the same letter being replaced by the same sentence.
    [Show full text]
  • Inversion by Definitional Reflection and the Admissibility of Logical Rules
    THE REVIEW OF SYMBOLIC LOGIC Volume 2, Number 3, September 2009 INVERSION BY DEFINITIONAL REFLECTION AND THE ADMISSIBILITY OF LOGICAL RULES WAGNER DE CAMPOS SANZ Faculdade de Filosofia, Universidade Federal de Goias´ THOMAS PIECHA Wilhelm-Schickard-Institut, Universitat¨ Tubingen¨ Abstract. The inversion principle for logical rules expresses a relationship between introduction and elimination rules for logical constants. Hallnas¨ & Schroeder-Heister (1990, 1991) proposed the principle of definitional reflection, which embodies basic ideas of inversion in the more general context of clausal definitions. For the context of admissibility statements, this has been further elaborated by Schroeder-Heister (2007). Using the framework of definitional reflection and its admis- sibility interpretation, we show that, in the sequent calculus of minimal propositional logic, the left introduction rules are admissible when the right introduction rules are taken as the definitions of the logical constants and vice versa. This generalizes the well-known relationship between introduction and elimination rules in natural deduction to the framework of the sequent calculus. §1. Inversion principle. The idea of inverting logical rules can be found in a well- known remark by Gentzen: “The introductions are so to say the ‘definitions’ of the sym- bols concerned, and the eliminations are ultimately only consequences hereof, what can approximately be expressed as follows: In eliminating a symbol, the formula concerned – of which the outermost symbol is in question – may only ‘be used as that what it means on the ground of the introduction of that symbol’.”1 The inversion principle itself was formulated by Lorenzen (1955) in the general context of rule-based systems and is thus not restricted to logical rules.
    [Show full text]
  • Logical Vs. Natural Language Conjunctions in Czech: a Comparative Study
    ITAT 2016 Proceedings, CEUR Workshop Proceedings Vol. 1649, pp. 68–73 http://ceur-ws.org/Vol-1649, Series ISSN 1613-0073, c 2016 K. Prikrylová,ˇ V. Kubon,ˇ K. Veselovská Logical vs. Natural Language Conjunctions in Czech: A Comparative Study Katrin Prikrylová,ˇ Vladislav Kubon,ˇ and Katerinaˇ Veselovská Charles University in Prague, Faculty of Mathematics and Physics Czech Republic {prikrylova,vk,veselovska}@ufal.mff.cuni.cz Abstract: This paper studies the relationship between con- ceptions and irregularities which do not abide the rules as junctions in a natural language (Czech) and their logical strictly as it is the case in logic. counterparts. It shows that the process of transformation Primarily due to this difference, the transformation of of a natural language expression into its logical representa- natural language sentences into their logical representation tion is not straightforward. The paper concentrates on the constitutes a complex issue. As we are going to show in most frequently used logical conjunctions, and , and it the subsequent sections, there are no simple rules which analyzes the natural language phenomena which∧ influence∨ would allow automation of the process – the majority of their transformation into logical conjunction and disjunc- problematic cases requires an individual approach. tion. The phenomena discussed in the paper are temporal In the following text we are going to restrict our ob- sequence, expressions describing mutual relationship and servations to the two most frequently used conjunctions, the consequences of using plural. namely a (and) and nebo (or). 1 Introduction and motivation 2 Sentences containing the conjunction a (and) The endeavor to express natural language sentences in the form of logical expressions is probably as old as logic it- The initial assumption about complex sentences contain- self.
    [Show full text]
  • Presupposition Projection and Entailment Relations
    Presupposition Projection and Entailment Relations Amaia Garcia Odon i Acknowledgements I would like to thank the members of my thesis committee, Prof. Dr. Rob van der Sandt, Dr. Henk Zeevat, Dr. Isidora Stojanovic, Dr. Cornelia Ebert and Dr. Enric Vallduví for having accepted to be on my thesis committee. I am extremely grateful to my adviser, Louise McNally, and to Rob van der Sandt. Without their invaluable help, I would not have written this dissertation. Louise has been a wonderful adviser, who has always provided me with excellent guid- ance and continuous encouragement. Rob has been a mentor who has generously spent much time with me, teaching me Logic, discussing my work, and helping me clear up my thoughts. Very special thanks to Henk Zeevat for having shared his insights with me and for having convinced me that it was possible to finish on time. Thanks to Bart Geurts, Noor van Leusen, Sammie Tarenskeen, Bob van Tiel, Natalia Zevakhina and Corien Bary in Nijmegen; to Paul Dekker, Jeroen Groe- nendijk, Frank Veltman, Floris Roelofsen, Morgan Mameni, Raquel Fernández and Margot Colinet in Amsterdam; to Fernando García Murga, Agustín Vicente, Myriam Uribe-Etxebarria, Javier Ormazabal, Vidal Valmala, Gorka Elordieta, Urtzi Etxeberria and Javi Fernández in Vitoria-Gasteiz, to David Beaver and Mari- bel Romero. Also thanks to the people I met at the ESSLLIs of Bordeaux, Copen- hagen and Ljubljana, especially to Nick Asher, Craige Roberts, Judith Tonhauser, Fenghui Zhang, Mingya Liu, Alexandra Spalek, Cornelia Ebert and Elena Pa- ducheva. I gratefully acknowledge the financial help provided by the Basque Government – Departamento de Educación, Universidades e Investigación (BFI07.96) and Fun- dación ICREA (via an ICREA Academia award to Louise McNally).
    [Show full text]
  • Logical Inference and Its Dynamics
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PhilPapers Logical Inference and Its Dynamics Carlotta Pavese 1 Duke University Philosophy Department Abstract This essay advances and develops a dynamic conception of inference rules and uses it to reexamine a long-standing problem about logical inference raised by Lewis Carroll's regress. Keywords: Inference, inference rules, dynamic semantics. 1 Introduction Inferences are linguistic acts with a certain dynamics. In the process of making an inference, we add premises incrementally, and revise contextual assump- tions, often even just provisionally, to make them compatible with the premises. Making an inference is, in this sense, moving from one set of assumptions to another. The goal of an inference is to reach a set of assumptions that supports the conclusion of the inference. This essay argues from such a dynamic conception of inference to a dynamic conception of inference rules (section x2). According to such a dynamic con- ception, inference rules are special sorts of dynamic semantic values. Section x3 develops this general idea into a detailed proposal and section x4 defends it against an outstanding objection. Some of the virtues of the dynamic con- ception of inference rules developed here are then illustrated by showing how it helps us re-think a long-standing puzzle about logical inference, raised by Lewis Carroll [3]'s regress (section x5). 2 From The Dynamics of Inference to A Dynamic Conception of Inference Rules Following a long tradition in philosophy, I will take inferences to be linguistic acts. 2 Inferences are acts in that they are conscious, at person-level, and 1 I'd like to thank Guillermo Del Pinal, Simon Goldstein, Diego Marconi, Ram Neta, Jim Pryor, Alex Rosenberg, Daniel Rothschild, David Sanford, Philippe Schlenker, Walter Sinnott-Armstrong, Seth Yalcin, Jack Woods, and three anonymous referees for helpful suggestions on earlier drafts.
    [Show full text]
  • List of Rules of Inference 1 List of Rules of Inference
    List of rules of inference 1 List of rules of inference This is a list of rules of inference, logical laws that relate to mathematical formulae. Introduction Rules of inference are syntactical transform rules which one can use to infer a conclusion from a premise to create an argument. A set of rules can be used to infer any valid conclusion if it is complete, while never inferring an invalid conclusion, if it is sound. A sound and complete set of rules need not include every rule in the following list, as many of the rules are redundant, and can be proven with the other rules. Discharge rules permit inference from a subderivation based on a temporary assumption. Below, the notation indicates such a subderivation from the temporary assumption to . Rules for classical sentential calculus Sentential calculus is also known as propositional calculus. Rules for negations Reductio ad absurdum (or Negation Introduction) Reductio ad absurdum (related to the law of excluded middle) Noncontradiction (or Negation Elimination) Double negation elimination Double negation introduction List of rules of inference 2 Rules for conditionals Deduction theorem (or Conditional Introduction) Modus ponens (or Conditional Elimination) Modus tollens Rules for conjunctions Adjunction (or Conjunction Introduction) Simplification (or Conjunction Elimination) Rules for disjunctions Addition (or Disjunction Introduction) Separation of Cases (or Disjunction Elimination) Disjunctive syllogism List of rules of inference 3 Rules for biconditionals Biconditional introduction Biconditional Elimination Rules of classical predicate calculus In the following rules, is exactly like except for having the term everywhere has the free variable . Universal Introduction (or Universal Generalization) Restriction 1: does not occur in .
    [Show full text]