
- 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
Matrix Grammars in Automata Theory
Matrix grammars represent an extension of context-free grammars, designed to handle more complex language structures by using grouped production rules. This is not like the traditional context-free grammars, where rules are applied individually. In matrix grammars, it requires rules be applied in a sequence, forming what is known as a matrix.
In this chapter, we will cover the basics of matrix grammars and dive into several examples to get this idea easily for a better understanding.
Basics of Matrix Grammars
A matrix grammar is a set of elements that together form the foundation for generating strings in a language. It is typically denoted as −
$$\mathrm{G \:=\: \{ V_N,\: \Sigma,\: M,\: S \}}$$
Where,
- $\mathrm{V_n}$ − Set of non-terminals (symbols that can be replaced)
- $\mathrm{\Sigma}$ − Set of terminals (symbols that appear in the final strings)
- $\mathrm{S}$ − Start symbol (the initial symbol from which the string generation begins)
- $\mathrm{M}$ − A finite non-empty set of sequences of production rules, known as matrices
Each matrix in the grammar is a sequence of production rules that must be applied together in a specific order. For example, a matrix might look like this −
$$\mathrm{m_i \:=\: [P_1,\: \rightarrow\: Q_1,\: P_2\: \rightarrow\: Q_2,\:..,\:P_r\: \rightarrow\: Q_r]}$$
Where each Pi is a non-terminal and Qi is either a non-terminal or a terminal.
Examples of Matrix Grammars
Here are a few examples of a simple matrix grammar −
Example 1: Matrix Grammar for the Language anbncn for n > 0
To better understand how matrix grammars work, let us see a simple example.
Consider the language anbncn, where n > 0. This language consists of strings with equal numbers of a's, b's, and c's, such as "abc", "aaabbbccc", and so on.
As we know the CFG, this language cannot be generated by a context-free grammar because it requires coordination between the three groups of characters.
Solution
The key to generating this language using a matrix grammar is to design the production rules so that one a, one b, and one c are added in each step.
Here's how we can do it −
- Start with the production S → XY.
- Then, group the following productions: [X → aXb, Y → cY]. This means that when X is replaced by aXb, Y is simultaneously replaced by cY.
- Finally, for the last step, group [X → ab, Y → c] to complete the string generation.
Thus, the matrix grammar is defined as −
$$\mathrm{G = \{ V_N, \Sigma, M, S \}}$$
Where,
- VN − {S, X, Y}
- Σ − {a, b, c}
- S − Start Symbol
- M − [S → XY], [X → aXb, Y → cY], [X → ab, Y → c]
This grammar ensures that for every a added, a corresponding b and c are also added, thus generating strings in the language anbncn.
Example 2: Matrix Grammar for the Language WW
Let us see another example, WW, where W is any string of a's and b's. The language consists of strings where a sequence W is followed by an identical sequence W. For example, "aa" and "abab", "abbaabba", etc., belong to this language.
Solution
To construct a matrix grammar for WW, we need to ensure that the same sequence W is generated twice. The length of the string in this language is either 0 or 2n, where n is the length of W.
- Start with S → XY, where X generates the first W and Y generates the last W.
- The productions for generating W are −
- S → aS | bS | ε (This generates W as a sequence of a's and b's)
- To ensure that X and Y generate the same sequence, group the productions as [X → aX, Y → aY] and [X → bX, Y → bY].
- Finally, for the end of the string, group [X → ε, Y → ε].
Thus, the matrix grammar is −
$$\mathrm{G \:=\: \{ V_N,\: \Sigma, \:M,\: S \}}$$
Where,
- VN − {S, X, Y}
- Σ − {a, b}
- S − Start Symbol
- M − [S → XY], [X → aX, Y → aY], [X → bX, Y → bY], [X → ε, Y → ε]
This ensures that W is generated twice in the sequence, this satisfies the condition for the language WW.
Example 3: Matrix Grammar for the Language anbncndn
Lastly, consider a more complex language anbncndn, where n > 0. This language consists of strings with equal numbers of a's, b's, c's, and d's.
Solution
This is also not solvable by standard context free grammar. To generate this language using a matrix grammar −
- Start with S → XY.
- Group the productions [X → aXb, Y → cYd] to add one a, one b, one c, and one d
- For the final replacement, use [X → ab, Y → cd].
Thus, the matrix grammar is −
$$\mathrm{G \:=\: \{ V_N,\: \Sigma,\: M,\: S \}}$$
Where,
- VN − {S, X, Y}
- Σ − {a, b, c, d}
- S − Start Symbol
- M − [S → XY], [X → aXb, Y → cYd], [X → ab, Y → cd]
This grammar ensures that for every a and b added, corresponding c and d are also added, generating strings in the language anbncndn.
Conclusion
Matrix grammars is a powerful extension of context-free grammars. In this chapter, we covered the basic structure of matrix grammars and three examples that demonstrate how they can be used to generate complex languages like anbncn, WW, and anbncndn.