Formal Languages and Automata Artificial Intelligence & Computer Vision Lab School of Computer Science and Engineering Seoul National University Course Information Instructor • Professor S.I. Yoo (유석인 교수) • Office : 302-321, E-mail: [email protected] Teaching Assistant • Suil Son (손수일) • Office : 302-317-1, E-mail: [email protected] Text • An Introduction to Formal Languages and Automata (Fourth Edition) (by Peter Linz) • Lecture Notes: http://ailab.snu.ac.kr/courses/am15.php Prerequisite • Discrete Mathematics in Computer Science Grading Policy • 3 Equally Weighted Exams (90%) + Class Attendance (10 %) AI & CV Lab, SNU Formal Languages & Automata Schedule 1. Introduction 2. Finite Automata and Regular Grammars 2.1 Finite Automata 2.2 Regular Languages and Regular Grammars 2.3 Properties of Regular Languages (Exam 1) 3. Pushdown Automata and Context-Free Grammars 3.1 Context-Free Languages 3.2 Simplification of Context-Free Grammars and Normal Forms 3.3 Pushdown Automata 3.4 Properties of Context-Free Languages (Exam 2) AI & CV Lab, SNU Formal Languages & Automata Schedule (cont.) 4. Turing Machines 4.1 Turing Machines 4.2 Other Models of Turing Machines 4.3 Unrestricted Grammars and Associated Languages 4.4 Limits of Algorithmic Computation (Exam 3) AI & CV Lab, SNU Formal Languages & Automata Formal Languages and Automata Artificial Intelligence & Computer Vision Lab School of Computer Science and Engineering Seoul National University 1. Introduction AI & CV Lab, SNU Formal Languages & Automata Automaton, Formal language, and Algorithm • Automaton (pl. Automata): An abstract model of the computer hardware consisting of indispensable features of a digital computer. • Formal language: An abstract model of general characteristics of programming languages consisting of a set of symbols and some rules of formation by which these symbols can be combined into entities called sentences. • Algorithm: A formalized concept of a mechanical computation or the computation by mechanical means. AI & CV Lab, SNU 6 Formal Languages & Automata Mathematical Preliminaries and Notation Definition: • Set • Relation − R ⊆ A × B (binary relation R from A to B) − R ⊆ A × A (binary relation R on A) − Reflexive, irreflexive, symmetric, asymmetric, antisymmetric, transitive • Function − A relation f from A to B is a function, denoted by f: A → B, if for every x in A, there is a unique y in B such that <x, y>∈ f − One to one (injective), onto (surjective), one to one and onto (bijective) AI & CV Lab, SNU 7 Formal Languages & Automata Mathematical Preliminaries and Notation Definition: • Graph − G = <N, E> where N is a set of vertices and E is a set of edges − Directed graph / undirected graph − A walk: a sequence of edges, a path: a walk with no edges repeated, a simple path: a path with no vertices repeated, a cycle with base v: a walk from one vertex v to itself, a simple cycle: a cycle with no vertices other than the base repeated, a loop: an edge from a vertex to itself • Tree − A directed graph that has no cycles, and that has one distinct node, called the root such that there is exactly one path from the root to every other vertex − Leaf/ parent/ child/ level/ height/ ordered tree AI & CV Lab, SNU 8 Formal Languages & Automata Mathematical Preliminaries and Notation (cont.) Proof by induction and proof by contradiction • Induction Basis : for some k ≥ 1, P1, P2, …, Pk are true. Induction step : for any n ≥ k, the truths of P1, P2, …, Pn imply the truth of Pn+1 • Contradiction To prove P, assume that P is false and derive that a conclusion is false. AI & CV Lab, SNU 9 Formal Languages & Automata Basic Concepts Definition: • Alphabet, Σ − a finite nonempty set of symbols • String on Σ − a finite sequence of symbols from Σ ex. Σ = {a, b, c}, string : aaa, ab, bc, ca, … − a substring (prefix and suffix of a string) • Concatenation of two strings − w1w2 = aaaab if w1 = aaa and w2= ab • Reverse of a string − if w = ab, wR = ba AI & CV Lab, SNU 10 Formal Languages & Automata Basic Concepts (cont.) Definition: • Length of a string − the number of symbols in the string − w = aaa, |w| = 3 • Empty string, λ − |λ| = 0 − wλ = λw = w, and w0 = λ • Σ+ = Σ* − {λ} where Σ is an alphabet. • Σ* = ∑n where Σ* is countably infinite if Σ is finite. ∞ n=0 AI & CV Lab, SNU 11 Formal Languages & Automata Basic Concepts (cont.) Definition: A language is a subset of Σ* where a string in a language is called a sentence Definition: A grammar is G=(V, T, S, P) where V = a finite set of variables, T = a finite set of terminal symbols, S ∈ V is a special symbol called the start variable, and P is a finite set of productions, x→y, where x ∈ (V ∪ T)+ and y ∈ (V ∪ T)*. AI & CV Lab, SNU 12 Formal Languages & Automata Basic Concepts (cont.) Definition: A language generated by G is L(G) such that * w}. L(G) = {w ∈ T*: S ⇒ If w ∈ L(G), then the sequence S⇒w1⇒w2⇒ …⇒wn⇒ w is a derivation of the sentence w where w is said to be derived from S. Each string of S, w1, w2,…, and w is called a sentential form of the derivation. ex. G = ({S}, {a, b}, S, P) where P: S → aSb, S → λ S ⇒ aSb ⇒ aaSbb ⇒ aabb * S ⇒ aabb L(G) = {λ, ab, aabb, aaabbb, … } = {anbn, n ≥ 0} Definition: Two grammars G1 and G2 are said to be equivalent if and only if L(G1) = L(G2). AI & CV Lab, SNU 13 Formal Languages & Automata Basic Concepts (cont.) Automata: Abstract model of a digital computer. Input file, Storage device, Control unit (be in some internal state). Configuration = Particular state of the control unit, input file, and temporary storage. Move = Transition of an automaton from one configuration to the next configuration. Input file Control unit storage output file AI & CV Lab, SNU 14 Formal Languages & Automata Automata Classification Structural Characteristics 1. Function of machine • Transducer (input and output tapes) • Accepter (no output tape) • Generator (no input tape) 2. Presence or a absence of a storage tape 3. Number of symbols in storage tape alphabet AI & CV Lab, SNU 15 Formal Languages & Automata Automata Classification (cont.) Behavioral Characteristics 1. Type of operation: • Deterministic • Nondeterministic 2. Motion if input tape head: • One way (left to right) • Two way (arbitrary motion) 3. Storage tape access restrictions: • Last in, first out (pushdown storage) • Arbitrary access within a region whose length is bounded • linearly by that of its input • Unrestricted access AI & CV Lab, SNU 16 Formal Languages & Automata Automata Classification (cont.) Finite state automata • Regular grammar − No storage tape Pushdown automata • Context-free grammar − One pushdown storage tape (Nondeterministic operation) Linear bounded automata • Context-sensitive grammar − One linear-bounded storage tape (Nondeterministic operation) Turing machine • Unrestricted grammar − One arbitrarily accessed storage tape AI & CV Lab, SNU 17 Formal Languages & Automata
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