
- Automata Theory - Applications
- Automata Terminology
- Basics of String in Automata
- Set Theory for Automata
- Finite Sets and Infinite Sets
- Algebraic Operations on Sets
- Relations Sets in Automata Theory
- Graph and Tree in Automata Theory
- Transition Table in Automata
- What is Queue Automata?
- Compound Finite Automata
- Complementation Process in DFA
- Closure Properties in Automata
- Concatenation Process in DFA
- Language and Grammars
- Language and Grammar
- Grammars in Theory of Computation
- Language Generated by a Grammar
- Chomsky Classification of Grammars
- Context-Sensitive Languages
- Finite Automata
- What is Finite Automata?
- Finite Automata Types
- Applications of Finite Automata
- Limitations of Finite Automata
- Two-way Deterministic Finite Automata
- Deterministic Finite Automaton (DFA)
- Non-deterministic Finite Automaton (NFA)
- NDFA to DFA Conversion
- Equivalence of NFA and DFA
- Dead State in Finite Automata
- Minimization of DFA
- Automata Moore Machine
- Automata Mealy Machine
- Moore vs Mealy Machines
- Moore to Mealy Machine
- Mealy to Moore Machine
- Myhill–Nerode Theorem
- Mealy Machine for 1’s Complement
- Finite Automata Exercises
- Complement of DFA
- Regular Expressions
- Regular Expression in Automata
- Regular Expression Identities
- Applications of Regular Expression
- Regular Expressions vs Regular Grammar
- Kleene Closure in Automata
- Arden’s Theorem in Automata
- Convert Regular Expression to Finite Automata
- Conversion of Regular Expression to DFA
- Equivalence of Two Finite Automata
- Equivalence of Two Regular Expressions
- Convert Regular Expression to Regular Grammar
- Convert Regular Grammar to Finite Automata
- Pumping Lemma in Theory of Computation
- Pumping Lemma for Regular Grammar
- Pumping Lemma for Regular Expression
- Pumping Lemma for Regular Languages
- Applications of Pumping Lemma
- Closure Properties of Regular Set
- Closure Properties of Regular Language
- Decision Problems for Regular Languages
- Decision Problems for Automata and Grammars
- Conversion of Epsilon-NFA to DFA
- Regular Sets in Theory of Computation
- Context-Free Grammars
- Context-Free Grammars (CFG)
- Derivation Tree
- Parse Tree
- Ambiguity in Context-Free Grammar
- CFG vs Regular Grammar
- Applications of Context-Free Grammar
- Left Recursion and Left Factoring
- Closure Properties of Context Free Languages
- Simplifying Context Free Grammars
- Removal of Useless Symbols in CFG
- Removal Unit Production in CFG
- Removal of Null Productions in CFG
- Linear Grammar
- Chomsky Normal Form (CNF)
- Greibach Normal Form (GNF)
- Pumping Lemma for Context-Free Grammars
- Decision Problems of CFG
- Pushdown Automata
- Pushdown Automata (PDA)
- Pushdown Automata Acceptance
- Deterministic Pushdown Automata
- Non-deterministic Pushdown Automata
- Construction of PDA from CFG
- CFG Equivalent to PDA Conversion
- Pushdown Automata Graphical Notation
- Pushdown Automata and Parsing
- Two-stack Pushdown Automata
- Turing Machines
- Basics of Turing Machine (TM)
- Representation of Turing Machine
- Examples of Turing Machine
- Turing Machine Accepted Languages
- Variations of Turing Machine
- Multi-tape Turing Machine
- Multi-head Turing Machine
- Multitrack Turing Machine
- Non-Deterministic Turing Machine
- Semi-Infinite Tape Turing Machine
- K-dimensional Turing Machine
- Enumerator Turing Machine
- Universal Turing Machine
- Restricted Turing Machine
- Convert Regular Expression to Turing Machine
- Two-stack PDA and Turing Machine
- Turing Machine as Integer Function
- Post–Turing Machine
- Turing Machine for Addition
- Turing Machine for Copying Data
- Turing Machine as Comparator
- Turing Machine for Multiplication
- Turing Machine for Subtraction
- Modifications to Standard Turing Machine
- Linear-Bounded Automata (LBA)
- Church's Thesis for Turing Machine
- Recursively Enumerable Language
- Computability & Undecidability
- Turing Language Decidability
- Undecidable Languages
- Turing Machine and Grammar
- Kuroda Normal Form
- Converting Grammar to Kuroda Normal Form
- Decidability
- Undecidability
- Reducibility
- Halting Problem
- Turing Machine Halting Problem
- Rice's Theorem in Theory of Computation
- Post’s Correspondence Problem (PCP)
- Types of Functions
- Recursive Functions
- Injective Functions
- Surjective Function
- Bijective Function
- Partial Recursive Function
- Total Recursive Function
- Primitive Recursive Function
- μ Recursive Function
- Ackermann’s Function
- Russell’s Paradox
- Gödel Numbering
- Recursive Enumerations
- Kleene's Theorem
- Kleene's Recursion Theorem
- Advanced Concepts
- Matrix Grammars
- Probabilistic Finite Automata
- Cellular Automata
- Reduction of CFG
- Reduction Theorem
- Regular expression to ∈-NFA
- Quotient Operation
- Parikh’s Theorem
- Ladner’s Theorem
Variations of Turing Machine in Automata Theory
Turing machines are powerful computational models that can simulate any algorithmic process. A standard Turing machine consists of a single tape and a single read-write head. However, there are variations of Turing machine that have been developed to address different computational challenges. These variations differ mainly in structure and operation, but they all have the same computational power as the standard Turing machine.
In this chapter, we will present an overview of these different types of Turing machines with diagrams for a better understanding.
Multi-tape Turing Machine
As the name suggests, a multi-tape Turing machine is an extension of the standard Turing machine where multiple tapes are available for input, output, and computation. Each tape has its own read-write head, and the machine's transition function is based on the current state and the symbols read by each head.

Example
Consider the problem of checking if a binary string is a palindrome or not. A two-tape Turing machine can solve this by copying the string from the first tape to the second, then comparing the string on both tapes in opposite directions.
The process involves copying input from one tape to another, tracing the first tape from left to right while moving the second tape from left to right, and comparing symbols. If all corresponding symbols match, the string is a palindrome, reducing time complexity compared to a single-tape machine.
Multi-head Turing Machine
In a multi-head Turing machine, a single tape is used, but it has multiple read-write heads. These heads can independently read and write symbols, enabling the machine to perform complex tasks more efficiently.

Example
The same palindrome checking can be done with this also. The heads start at both ends of the string and move towards each other, checking if the corresponding symbols are identical.
- If multiple heads attempt to write different symbols to the same cell, a priority system determines which head's action is executed.
- If a head tries to move left beyond the leftmost cell, a special condition known as "hanging" occurs, which needs to be handled.
- The multi-head Turing machine, like the multi-tape machine, can be converted to an equivalent single-head machine, although the process may be more complex.
Two-way Infinite Tape Turing Machine
A two-way infinite tape Turing machine allows the tape to extend infinitely in both directions, unlike the standard machine where the tape extends infinitely in only one direction. This removes the boundary on the left side of the tape.

Benefits
This variation can be particularly useful in computations where data may need to be processed symmetrically around a central point.
It can be simulated by a standard Turing machine by marking the left boundary and preventing the head from moving beyond it.
K-dimensional Turing Machine
A K-dimensional Turing machine extends the concept of the tape to multiple dimensions. For example, a two-dimensional Turing machine as given in the following diagram, has a tape that extends infinitely in both the X and Y directions.

In a two-dimensional Turing machine, the read-write head can move not only left and right but also up and down. This added flexibility allows the machine to process more complex data structures, such as matrices or grids. This type of Turing machine is ideal for tasks that naturally fit a multi-dimensional space, such as image processing or simulations of physical phenomena.
Non-deterministic Turing Machine
A non-deterministic Turing machine (NDTM) differs from a deterministic Turing machine in that it can transition into multiple possible states from a given state and symbol. Instead of following a single path, the NDTM can explore many computational paths simultaneously.
Example
Consider the language L = {0n 1m} where n and m are not necessarily equal. An NDTM can non-deterministically choose how many 0's and 1's to match, accepting the string if it satisfies the condition.
Every non-deterministic Turing machine has an equivalent deterministic Turing machine. The deterministic version simulates all possible paths of the NDTM and accepts the string if any path leads to an accepting state.
Enumerator Turing Machine
An enumerator Turing machine is designed to generate strings of a language. It is equipped with a work tape and an output tape. The machine writes symbols to the output tape, which is then printed.

The enumerator repeatedly writes symbols on the output tape based on its transition functions. Once a string is completed, it is printed, and the machine resets to generate the next string.
This type of Turing machine is useful for tasks where the goal is to list all valid strings of a language, such as generating all possible solutions to a problem.
Conclusion
Turing machines, in all their variations, are built due to the flexibility and power of computational models. Whether it's the parallel processing capabilities of multi-tape and multi-head machines, the extended reach of infinite tape machines, or the exploratory nature of non-deterministic machines, each variant has its strengths and weaknesses. Through these variations, Turing Machines can solve a large variation of problems efficiently.