
- 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
Modifications to Standard Turing Machine
The Turing machine is a fundamental model of computation that simulates the logic of any algorithmic process. The standard Turing machine consists of a single tape and a single read-write head. While this model is powerful, various modifications have been proposed over the years to address specific computational challenges and to explore different aspects of computation. These modifications, though differing in structure and operation, retain the same computational power as the standard Turing machine. In this chapter, we will highlight some of these key modifications.
Multi-tape Turing Machine
The multi-tape Turing machine is an extension of the standard model where multiple tapes are used, each with its own read-write head. This setup allows for more efficient computation by enabling simultaneous reading and writing on different tapes.

For example, a two-tape Turing machine can check if a binary string is a palindrome by copying the string from one tape to another and comparing it in opposite directions. This process involves tracing the first tape from left to right, 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, there is still only one tape, but multiple read-write heads are used. These heads can operate independently, reading and writing on the tape at different positions simultaneously. This modification is particularly useful for complex tasks where parallel processing is beneficial.

For instance, palindrome checking can be performed using multi-head Turing machines, which start at both ends of a string and move towards each other to check if corresponding symbols are identical.
A priority system determines which head's action is executed when multiple heads attempt to write different symbols to the same cell. A special condition called "hanging" needs to be handled when a head moves left beyond the leftmost cell.
Two-way Infinite Tape Turing Machine
The standard Turing machine's tape extends infinitely in only one direction. The two-way infinite tape Turing machine removes this restriction, allowing the tape to extend infinitely in both directions. This variation is useful for computations that require symmetrical data processing around a central point.
Although it can be simulated by a standard Turing machine by marking the left boundary, the two-way infinite tape provides a more natural model for certain problems.

The modification is useful for symmetrical data processing around a central point, and can be simulated by a Turing machine by marking the left boundary and preventing the head from moving beyond it.
K-dimensional Turing Machine
A K-dimensional Turing machine generalizes the concept of the tape to multiple dimensions. For example, in a two-dimensional Turing machine, the tape extends infinitely in both the X and Y directions.
The read-write head can move not just left and right, but also up and down. This modification is ideal for processing data structures like matrices or grids and is particularly suited for tasks in image processing or physical simulations.

Non-deterministic Turing Machine
Unlike a deterministic Turing machine, which follows a single path of execution, a non-deterministic Turing machine (NDTM) can explore multiple computational paths simultaneously. In each state, the machine can transition into multiple possible states based on the current symbol and state. This allows the NDTM to explore many possible solutions at once.
For instance, in language recognition problems, an NDTM can guess the number of symbols to match and explore all possibilities simultaneously. Although every NDTM has an equivalent deterministic version, the non-deterministic model is often easier to conceptualize for certain problems.
For example, the, language L = {0n ,1m} is a non-deterministic Turing machine that can match 0's and 1's non-deterministically. It accepts a string if it meets a condition. Every Turing machine has an equivalent deterministic Turing machine, which simulates all possible paths and accepts the string if any path leads to an accepting state.
Enumerator Turing Machine
An enumerator Turing machine is designed to generate all possible strings of a language. It operates with a work tape and an output tape, writing symbols to 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 modification is particularly useful in tasks where the goal is to list all valid strings of a language, such as generating solutions to a problem.

Conclusion
In this chapter, we explained several modifications over the standard single-tape Turing Machine. These modifications make the standard Turing Machine more advanced for several tasks, for example, multi-tape and multi-head Turing Machines could easily solve complex problems.