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Chapter 2 Introduction to Classical Propositional
CHAPTER 2 INTRODUCTION TO CLASSICAL PROPOSITIONAL LOGIC 1 Motivation and History The origins of the classical propositional logic, classical propositional calculus, as it was, and still often is called, go back to antiquity and are due to Stoic school of philosophy (3rd century B.C.), whose most eminent representative was Chryssipus. But the real development of this calculus began only in the mid-19th century and was initiated by the research done by the English math- ematician G. Boole, who is sometimes regarded as the founder of mathematical logic. The classical propositional calculus was ¯rst formulated as a formal ax- iomatic system by the eminent German logician G. Frege in 1879. The assumption underlying the formalization of classical propositional calculus are the following. Logical sentences We deal only with sentences that can always be evaluated as true or false. Such sentences are called logical sentences or proposi- tions. Hence the name propositional logic. A statement: 2 + 2 = 4 is a proposition as we assume that it is a well known and agreed upon truth. A statement: 2 + 2 = 5 is also a proposition (false. A statement:] I am pretty is modeled, if needed as a logical sentence (proposi- tion). We assume that it is false, or true. A statement: 2 + n = 5 is not a proposition; it might be true for some n, for example n=3, false for other n, for example n= 2, and moreover, we don't know what n is. Sentences of this kind are called propositional functions. We model propositional functions within propositional logic by treating propositional functions as propositions. -
COMPSCI 501: Formal Language Theory Insights on Computability Turing Machines Are a Model of Computation Two (No Longer) Surpris
Insights on Computability Turing machines are a model of computation COMPSCI 501: Formal Language Theory Lecture 11: Turing Machines Two (no longer) surprising facts: Marius Minea Although simple, can describe everything [email protected] a (real) computer can do. University of Massachusetts Amherst Although computers are powerful, not everything is computable! Plus: “play” / program with Turing machines! 13 February 2019 Why should we formally define computation? Must indeed an algorithm exist? Back to 1900: David Hilbert’s 23 open problems Increasingly a realization that sometimes this may not be the case. Tenth problem: “Occasionally it happens that we seek the solution under insufficient Given a Diophantine equation with any number of un- hypotheses or in an incorrect sense, and for this reason do not succeed. known quantities and with rational integral numerical The problem then arises: to show the impossibility of the solution under coefficients: To devise a process according to which the given hypotheses or in the sense contemplated.” it can be determined in a finite number of operations Hilbert, 1900 whether the equation is solvable in rational integers. This asks, in effect, for an algorithm. Hilbert’s Entscheidungsproblem (1928): Is there an algorithm that And “to devise” suggests there should be one. decides whether a statement in first-order logic is valid? Church and Turing A Turing machine, informally Church and Turing both showed in 1936 that a solution to the Entscheidungsproblem is impossible for the theory of arithmetic. control To make and prove such a statement, one needs to define computability. In a recent paper Alonzo Church has introduced an idea of “effective calculability”, read/write head which is equivalent to my “computability”, but is very differently defined. -
CAS LX 522 Syntax I
It is likely… CAS LX 522 IP This satisfies the EPP in Syntax I both clauses. The main DPj I′ clause has Mary in SpecIP. Mary The embedded clause has Vi+I VP is the trace in SpecIP. V AP Week 14b. PRO and control ti This specific instance of A- A IP movement, where we move a likely subject from an embedded DP I′ clause to a higher clause is tj generally called subject raising. I VP to leave Reluctance to leave Reluctance to leave Now, consider: Reluctant has two θ-roles to assign. Mary is reluctant to leave. One to the one feeling the reluctance (Experiencer) One to the proposition about which the reluctance holds (Proposition) This looks very similar to Mary is likely to leave. Can we draw the same kind of tree for it? Leave has one θ-role to assign. To the one doing the leaving (Agent). How many θ-roles does reluctant assign? In Mary is reluctant to leave, what θ-role does Mary get? IP Reluctance to leave Reluctance… DPi I′ Mary Vj+I VP In Mary is reluctant to leave, is V AP Mary is doing the leaving, gets Agent t Mary is reluctant to leave. j t from leave. i A′ Reluctant assigns its θ- Mary is showing the reluctance, gets θ roles within AP as A θ IP Experiencer from reluctant. required, Mary moves reluctant up to SpecIP in the main I′ clause by Spellout. ? And we have a problem: I vP But what gets the θ-role to Mary appears to be getting two θ-roles, from leave, and what v′ in violation of the θ-criterion. -
Thinking Recursively Part Four
Thinking Recursively Part Four Announcements ● Assignment 2 due right now. ● Assignment 3 out, due next Monday, April 30th at 10:00AM. ● Solve cool problems recursively! ● Sharpen your recursive skillset! A Little Word Puzzle “What nine-letter word can be reduced to a single-letter word one letter at a time by removing letters, leaving it a legal word at each step?” Shrinkable Words ● Let's call a word with this property a shrinkable word. ● Anything that isn't a word isn't a shrinkable word. ● Any single-letter word is shrinkable ● A, I, O ● Any multi-letter word is shrinkable if you can remove a letter to form a word, and that word itself is shrinkable. ● So how many shrinkable words are there? Recursive Backtracking ● The function we wrote last time is an example of recursive backtracking. ● At each step, we try one of many possible options. ● If any option succeeds, that's great! We're done. ● If none of the options succeed, then this particular problem can't be solved. Recursive Backtracking if (problem is sufficiently simple) { return whether or not the problem is solvable } else { for (each choice) { try out that choice. if it succeeds, return success. } return failure } Failure in Backtracking S T A R T L I N G Failure in Backtracking S T A R T L I N G S T A R T L I G Failure in Backtracking S T A R T L I N G S T A R T L I G Failure in Backtracking S T A R T L I N G Failure in Backtracking S T A R T L I N G S T A R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G Failure in Backtracking S T A R T L I N G S T A R T I N G S T A R I N G Failure in Backtracking ● Returning false in recursive backtracking does not mean that the entire problem is unsolvable! ● Instead, it just means that the current subproblem is unsolvable. -
Syntax and Semantics of Propositional Logic
INTRODUCTIONTOLOGIC Lecture 2 Syntax and Semantics of Propositional Logic. Dr. James Studd Logic is the beginning of wisdom. Thomas Aquinas Outline 1 Syntax vs Semantics. 2 Syntax of L1. 3 Semantics of L1. 4 Truth-table methods. Examples of syntactic claims ‘Bertrand Russell’ is a proper noun. ‘likes logic’ is a verb phrase. ‘Bertrand Russell likes logic’ is a sentence. Combining a proper noun and a verb phrase in this way makes a sentence. Syntax vs. Semantics Syntax Syntax is all about expressions: words and sentences. ‘Bertrand Russell’ is a proper noun. ‘likes logic’ is a verb phrase. ‘Bertrand Russell likes logic’ is a sentence. Combining a proper noun and a verb phrase in this way makes a sentence. Syntax vs. Semantics Syntax Syntax is all about expressions: words and sentences. Examples of syntactic claims ‘likes logic’ is a verb phrase. ‘Bertrand Russell likes logic’ is a sentence. Combining a proper noun and a verb phrase in this way makes a sentence. Syntax vs. Semantics Syntax Syntax is all about expressions: words and sentences. Examples of syntactic claims ‘Bertrand Russell’ is a proper noun. ‘Bertrand Russell likes logic’ is a sentence. Combining a proper noun and a verb phrase in this way makes a sentence. Syntax vs. Semantics Syntax Syntax is all about expressions: words and sentences. Examples of syntactic claims ‘Bertrand Russell’ is a proper noun. ‘likes logic’ is a verb phrase. Combining a proper noun and a verb phrase in this way makes a sentence. Syntax vs. Semantics Syntax Syntax is all about expressions: words and sentences. Examples of syntactic claims ‘Bertrand Russell’ is a proper noun. -
Chapter 6 Formal Language Theory
Chapter 6 Formal Language Theory In this chapter, we introduce formal language theory, the computational theories of languages and grammars. The models are actually inspired by formal logic, enriched with insights from the theory of computation. We begin with the definition of a language and then proceed to a rough characterization of the basic Chomsky hierarchy. We then turn to a more de- tailed consideration of the types of languages in the hierarchy and automata theory. 6.1 Languages What is a language? Formally, a language L is defined as as set (possibly infinite) of strings over some finite alphabet. Definition 7 (Language) A language L is a possibly infinite set of strings over a finite alphabet Σ. We define Σ∗ as the set of all possible strings over some alphabet Σ. Thus L ⊆ Σ∗. The set of all possible languages over some alphabet Σ is the set of ∗ all possible subsets of Σ∗, i.e. 2Σ or ℘(Σ∗). This may seem rather simple, but is actually perfectly adequate for our purposes. 6.2 Grammars A grammar is a way to characterize a language L, a way to list out which strings of Σ∗ are in L and which are not. If L is finite, we could simply list 94 CHAPTER 6. FORMAL LANGUAGE THEORY 95 the strings, but languages by definition need not be finite. In fact, all of the languages we are interested in are infinite. This is, as we showed in chapter 2, also true of human language. Relating the material of this chapter to that of the preceding two, we can view a grammar as a logical system by which we can prove things. -
Notes on Mathematical Logic David W. Kueker
Notes on Mathematical Logic David W. Kueker University of Maryland, College Park E-mail address: [email protected] URL: http://www-users.math.umd.edu/~dwk/ Contents Chapter 0. Introduction: What Is Logic? 1 Part 1. Elementary Logic 5 Chapter 1. Sentential Logic 7 0. Introduction 7 1. Sentences of Sentential Logic 8 2. Truth Assignments 11 3. Logical Consequence 13 4. Compactness 17 5. Formal Deductions 19 6. Exercises 20 20 Chapter 2. First-Order Logic 23 0. Introduction 23 1. Formulas of First Order Logic 24 2. Structures for First Order Logic 28 3. Logical Consequence and Validity 33 4. Formal Deductions 37 5. Theories and Their Models 42 6. Exercises 46 46 Chapter 3. The Completeness Theorem 49 0. Introduction 49 1. Henkin Sets and Their Models 49 2. Constructing Henkin Sets 52 3. Consequences of the Completeness Theorem 54 4. Completeness Categoricity, Quantifier Elimination 57 5. Exercises 58 58 Part 2. Model Theory 59 Chapter 4. Some Methods in Model Theory 61 0. Introduction 61 1. Realizing and Omitting Types 61 2. Elementary Extensions and Chains 66 3. The Back-and-Forth Method 69 i ii CONTENTS 4. Exercises 71 71 Chapter 5. Countable Models of Complete Theories 73 0. Introduction 73 1. Prime Models 73 2. Universal and Saturated Models 75 3. Theories with Just Finitely Many Countable Models 77 4. Exercises 79 79 Chapter 6. Further Topics in Model Theory 81 0. Introduction 81 1. Interpolation and Definability 81 2. Saturated Models 84 3. Skolem Functions and Indescernables 87 4. Some Applications 91 5. -
Formal Languages
Formal Languages Discrete Mathematical Structures Formal Languages 1 Strings Alphabet: a finite set of symbols – Normally characters of some character set – E.g., ASCII, Unicode – Σ is used to represent an alphabet String: a finite sequence of symbols from some alphabet – If s is a string, then s is its length ¡ ¡ – The empty string is symbolized by ¢ Discrete Mathematical Structures Formal Languages 2 String Operations Concatenation x = hi, y = bye ¡ xy = hibye s ¢ = s = ¢ s ¤ £ ¨© ¢ ¥ § i 0 i s £ ¥ i 1 ¨© s s § i 0 ¦ Discrete Mathematical Structures Formal Languages 3 Parts of a String Prefix Suffix Substring Proper prefix, suffix, or substring Subsequence Discrete Mathematical Structures Formal Languages 4 Language A language is a set of strings over some alphabet ¡ Σ L Examples: – ¢ is a language – ¢ is a language £ ¤ – The set of all legal Java programs – The set of all correct English sentences Discrete Mathematical Structures Formal Languages 5 Operations on Languages Of most concern for lexical analysis Union Concatenation Closure Discrete Mathematical Structures Formal Languages 6 Union The union of languages L and M £ L M s s £ L or s £ M ¢ ¤ ¡ Discrete Mathematical Structures Formal Languages 7 Concatenation The concatenation of languages L and M £ LM st s £ L and t £ M ¢ ¤ ¡ Discrete Mathematical Structures Formal Languages 8 Kleene Closure The Kleene closure of language L ∞ ¡ i L £ L i 0 Zero or more concatenations Discrete Mathematical Structures Formal Languages 9 Positive Closure The positive closure of language L ∞ i L £ L -
Syntax: Recursion, Conjunction, and Constituency
Syntax: Recursion, Conjunction, and Constituency Course Readings Recursion Syntax: Conjunction Recursion, Conjunction, and Constituency Tests Auxiliary Verbs Constituency . Syntax: Course Readings Recursion, Conjunction, and Constituency Course Readings Recursion Conjunction Constituency Tests The following readings have been posted to the Moodle Auxiliary Verbs course site: I Language Files: Chapter 5 (pp. 204-215, 216-220) I Language Instinct: Chapter 4 (pp. 74-99) . Syntax: An Interesting Property of our PS Rules Recursion, Conjunction, and Our Current PS Rules: Constituency S ! f NP , CP g VP NP ! (D) (A*) N (CP) (PP*) Course Readings VP ! V (NP) f (NP) (CP) g (PP*) Recursion PP ! P (NP) Conjunction CP ! C S Constituency Tests Auxiliary Verbs An Interesting Feature of These Rules: As we saw last time, these rules allow sentences to contain other sentences. I A sentence must have a VP in it. I A VP can have a CP in it. I A CP must have an S in it. Syntax: An Interesting Property of our PS Rules Recursion, Conjunction, and Our Current PS Rules: Constituency S ! f NP , CP g VP NP ! (D) (A*) N (CP) (PP*) Course Readings VP ! V (NP) f (NP) (CP) g (PP*) Recursion ! PP P (NP) Conjunction CP ! C S Constituency Tests Auxiliary Verbs An Interesting Feature of These Rules: As we saw last time, these rules allow sentences to contain other sentences. S NP VP N V CP Dave thinks C S that . he. is. cool. Syntax: An Interesting Property of our PS Rules Recursion, Conjunction, and Our Current PS Rules: Constituency S ! f NP , CP g VP NP ! (D) (A*) N (CP) (PP*) Course Readings VP ! V (NP) f (NP) (CP) g (PP*) Recursion PP ! P (NP) Conjunction CP ! CS Constituency Tests Auxiliary Verbs Another Interesting Feature of These Rules: These rules also allow noun phrases to contain other noun phrases. -
Propositional Logic: Semantics and an Example
Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Propositional Logic: Semantics and an Example CPSC 322 { Logic 2 Textbook x5.2 Propositional Logic: Semantics and an Example CPSC 322 { Logic 2, Slide 1 Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Lecture Overview 1 Recap: Syntax 2 Propositional Definite Clause Logic: Semantics 3 Using Logic to Model the World 4 Proofs Propositional Logic: Semantics and an Example CPSC 322 { Logic 2, Slide 2 Definition (body) A body is an atom or is of the form b1 ^ b2 where b1 and b2 are bodies. Definition (definite clause) A definite clause is an atom or is a rule of the form h b where h is an atom and b is a body. (Read this as \h if b.") Definition (knowledge base) A knowledge base is a set of definite clauses Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Propositional Definite Clauses: Syntax Definition (atom) An atom is a symbol starting with a lower case letter Propositional Logic: Semantics and an Example CPSC 322 { Logic 2, Slide 3 Definition (definite clause) A definite clause is an atom or is a rule of the form h b where h is an atom and b is a body. (Read this as \h if b.") Definition (knowledge base) A knowledge base is a set of definite clauses Recap: Syntax PDC: Semantics Using Logic to Model the World Proofs Propositional Definite Clauses: Syntax Definition (atom) An atom is a symbol starting with a lower case letter Definition (body) A body is an atom or is of the form b1 ^ b2 where b1 and b2 are bodies. -
Symbol Sense: Informal Sense-Making in Formal Mathematics
Symbol Sense: Informal Sense-making in Formal Mathematics ABRAHAM ARCA VI Prologue Irineo Funes, the "memorious ", was created from Borges ~s fan tions and formulating mathematical arguments (proofs) tastic palette He had an extraordinarity retentive memory, he Instruction does not always provide opportunities not only was able to remember everything, be it the shapes of the southern not to memorize, but also to "forget" rules and details and clouds on the dawn of April 30th, 1882, or all the words in to be able to see through them in order to think, abstract, Engli'>h, French, Portuguese and Latin Each one of his experi generalize, and plan solution strategies Therefore, it ences was fully and accurately registered in his infinite memory would seem reasonable to attempt a description of a paral On one occasion he thought of "reducing" each past day of his lel notion to that of "number sense" in arithmetic: the idea life to s·eventy thousand remembrances to be referred to by num of "symbol sense".[!] bers. Two considerations dissuaded him: the task was inter minable and futile. Towards the end of the story, the real tragedy What is symbol sense? A first round of Funes is revealed He was incapable of general, "platonic " Compared to the attention which has been given to "num ideas For example, it was hard for him to understand that the ber sense", there is very little in the literature on "'symbol generic name dog included so many individuals of diverse sizes sense". One welcome exception is Fey [1990] He does not and shapes Moreover, -
Logic Programming Lecture 19 Tuesday, April 5, 2016 1 Logic Programming 2 Prolog
Harvard School of Engineering and Applied Sciences — CS 152: Programming Languages Logic programming Lecture 19 Tuesday, April 5, 2016 1 Logic programming Logic programming has its roots in automated theorem proving. Logic programming differs from theorem proving in that logic programming uses the framework of a logic to specify and perform computation. Essentially, a logic program computes values, using mechanisms that are also useful for deduction. Logic programming typically restricts itself to well-behaved fragments of logic. We can think of logic programs as having two interpretations. In the declarative interpretation, a logic pro- gram declares what is being computed. In the procedural interpretation, a logic program program describes how a computation takes place. In the declarative interpretation, one can reason about the correctness of a program without needing to think about underlying computation mechanisms; this makes declarative programs easier to understand, and to develop. A lot of the time, once we have developed a declarative program using a logic programming language, we also have an executable specification, that is, a procedu- ral interpretation that tells us how to compute what we described. This is one of the appealing features of logic programming. (In practice, executable specifications obtained this way are often inefficient; an under- standing of the underlying computational mechanism is needed to make the execution of the declarative program efficient.) We’ll consider some of the concepts of logic programming by considering the programming language Prolog, which was developed in the early 70s, initially as a programming language for natural language processing. 2 Prolog We start by introducing some terminology and syntax.