LINEAR BOUNDED AUTOMATA (LBA).pptx
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Linear bounded automata are a type of automaton that operates like a Turing machine but with a tape bounded by the length of the input string. They are more powerful than pushdown automata but less powerful than Turing machines. The computation is restricted to a constant bounded area between left and right end markers on the tape. Linear bounded automata accept context sensitive languages.
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A Turing machine is a mathematical model of computation that consists of an infinite tape divided into cells, a head that reads and writes symbols on the tape, finite states, and transition rules. It was invented by Alan Turing in 1936 to formalize the idea of an algorithm. A Turing machine operates by reading a symbol on the tape, updating its state and writing a new symbol based on transition rules, then moving the head left or right. If it reaches an accepting state, the input is accepted. A language is recursively enumerable if it is accepted by a Turing machine.
QB104544.pdf
A Turing machine is a mathematical model of computation that consists of an infinite tape divided into cells, a head that reads and writes symbols on the tape, a finite set of states, and transition rules determining the behavior of the machine. In each step, the machine reads the symbol under the head, changes state according to the transition rules, writes a symbol, and moves the head left or right. The language accepted by a Turing machine is the set of strings that cause the machine to enter an accepting state. Turing machines can be constructed using techniques like storing information in the finite control, using multiple tracks or tapes, and implementing subroutines.
Finite state Transducers and mealy Machine
This document discusses finite state transducers. It provides definitions and explanations of the following key points:
- A finite state transducer is a finite automaton that maps input symbols to output symbols through state transitions. The input and output are separate tapes.
- They are formally defined as 7-tuples with states, input/output alphabets, transition functions, initial/final states. Transitions contain input-output pairs that map an input to a corresponding output.
- Deterministic transducers have a unique output for each input-state pair. Non-deterministic transducers can have multiple possible outputs.
- Mealy machines are a type of transducer where the output depends on both
Theory of computation and automata
This document discusses theory of computation and finite automata. It begins by defining theory of computation as dealing with the logic of computation using abstract machines called automata. It then defines basic terminology like symbols, alphabets, strings, and languages. Next, it introduces finite automata as the simplest machines that recognize patterns using a finite set of states. Deterministic finite automata and nondeterministic finite automata are described as the two types of finite automata, differing in their transition functions. Transition diagrams and tables are also presented as ways to represent finite automata.
Theory of computation and automata
The document provides an introduction to theory of computation and automata. It defines key concepts such as symbols, alphabets, strings, languages, finite automata, deterministic finite automata (DFA), non-deterministic finite automata (NFA). It explains these concepts using examples and discusses their representation using transition diagrams, transition tables, and examples of DFAs recognizing specific languages.
03-FiniteAutomata.pptx
Finite-state machines are finite collections of states with transition rules that take you from one state to another. They can be represented as graphs with nodes as states and directed arcs indicating transitions. Deterministic finite automata (DFAs) are finite-state machines where transitions are defined by reading input characters left to right. Nondeterministic finite automata (NFAs) allow transitions from a state to multiple states on a given input. While NFAs are more expressive, any NFA can be converted to an equivalent DFA using the subset construction algorithm.
TOA.docx
Turing Machine (TM) is a mathematical model which consists of an infinite length tape divided into cells on which input is given. It consists of a head which reads the input tape. A state register stores the state of the Turing machine. After reading an input symbol, it is replaced with another symbol, its internal state is changed, and it moves from one cell to the right or left. If the TM reaches the final state, the input string is accepted, otherwise rejected.
TOA.docx
Turing Machine (TM) is a mathematical model which consists of an infinite length tape divided into cells on which input is given. It consists of a head which reads the input tape. A state register stores the state of the Turing machine. After reading an input symbol, it is replaced with another symbol, its internal state is changed, and it moves from one cell to the right or left. If the TM reaches the final state, the input string is accepted, otherwise rejected.
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- 1. LINEAR BOUNDED AUTOMATA (LBA) SHAHANA P ROLL NO : 27
- 2. It behaves as a Turing machine but storage space of tape is restricted only to length of input string. It is less powerful than TM and more powerful than PDA. The computation is restricted to the constant bounded area. The input alphabet contain two special symbols which serve as left end marker and right end marker which means the transition neither move to left of the left end marker nor to right of the right end marker. Linear bounded automata accepts context sensitive language.
- 3. A linear bounded automaton can be defined as an 8-tuple (Q, X, ∑, q0, ML, MR, δ, F) where − •Q is a finite set of states •X is the tape alphabet •∑ is the input alphabet •q0 is the initial state •ML is the left end marker •MR is the right end marker where MR ≠ ML •δ is a transition function which maps each pair (state, tape symbol) to (state, tape symbol, Constant ‘c’) where c can be 0 or +1 or -1 •F is the set of final states
- 5. LBA – APPLICATIONS For implementation of genetic programming For constructing syntactic parse tree for semantic analysis for the compiler EXPRESSIVE POWER OF VARIOUS AUTOMATA Expressive power can be determined from the class or set of languages accepted by that particular type of machine. Here is the increasing sequence of expressive power of machines.
- 6. THANK YOU !