Automata theory: Unraveling the Methods and Embracing the Madness

Automata theory is the study of abstract computing devices [ Abstract computing devices are imaginary tools that help us understand the basic ideas behind how computers solve problems ], or “machines.” Before there were computers, in the 1930s, A. Turing studied an abstract machine that had all the capabilities of today’s computers, at least as far as what they could compute.

In the 1940s and 1950s, simpler kinds of machines, which we today call “finite automata,” were studied by a number of researchers. These automata, originally proposed to model brain function, turned out to be extremely useful for a variety of other purposes

In the late 1950s, the linguist N. Chomsky began the study of formal “grammars.” While not strictly machines, these grammars have close relationships to abstract automata and serve today as the basis of some important software components, including parts of compilers.

In 1969, S. Cook extended Turing’s study of what could and what could not be computed. Cook was able to separate those problems that can be solved efficiently by computer from those problems that can in principle be solved, but in practice take so much time that computers are useless for all but very small instances of the problem. The latter class of problems is called “intractable,” or “NP-hard.” It is highly unlikely that even the exponential improvement in computing speed that computer hardware has been following (“Moore’s Law”) will have significant impact on our ability to solve large instances of intractable problems.

Why Study Automata Theory?

Studying Automata Theory offers several valuable insights and benefits for individuals interested in computer science, theoretical computer science, and related fields. This mathematical framework focuses on understanding the behavior of abstract machines, which has numerous real-world applications in designing and analyzing computational systems. Here are some compelling reasons to study Automata Theory:

  1. Fundamental Understanding of Computation: Automata Theory lays the foundation for understanding the fundamental concepts of computation. It helps you grasp the concept of computation models, formal languages, and the limits of what can and cannot be computed.
  2. Language and Compiler Design: Automata Theory plays a crucial role in designing programming languages, compilers, and interpreters. By understanding formal grammars, regular expressions, and context-free grammars, you can create efficient and reliable programming languages and translation processes.
  3. Software Engineering: Knowledge of Automata Theory can improve your software engineering skills. It aids in developing robust and efficient algorithms, data structures, and software systems by providing insights into optimization and automation.
  4. Verification and Validation: Automata Theory is used to verify and validate the correctness of software and hardware systems. By modeling systems as automata, you can mathematically prove their correctness, security, and reliability.
  5. Algorithm Design and Analysis: Automata Theory offers techniques for analyzing the time and space complexity of algorithms. This knowledge is essential for designing efficient algorithms and understanding their performance characteristics.
  6. Artificial Intelligence and Machine Learning: Many AI and machine learning techniques are based on concepts from Automata Theory. Understanding automata helps you comprehend the underlying principles of neural networks, automata-based models, and pattern recognition.
  7. Formal Methods in Software Development: Formal methods involve mathematically rigorous techniques for software specification, design, and verification. Automata Theory provides a formal framework for expressing and reasoning about system specifications and behaviors.
  8. Cryptography and Security: Automata Theory contributes to cryptographic protocol analysis and security analysis. It helps in analyzing the vulnerabilities of security protocols and designing secure communication systems.
  9. Parallel and Distributed Computing: Concepts from Automata Theory are applicable to parallel and distributed computing models. Understanding automata-based models can aid in designing efficient parallel algorithms and distributed systems.
  10. Academic and Research Pursuits: Automata Theory is a central topic in theoretical computer science and is the foundation for various advanced areas of research. It offers a solid base for those pursuing further academic studies in computer science.

Introduction to Finite Automata

Finite Automata, often abbreviated as FA, are pivotal mathematical models within computer science and theoretical computer science. These abstract machines play a vital role in analyzing computational system behavior, making them fundamental to language recognition, algorithm design, and software engineering.

At its core, a Finite Automaton is a potent device designed to process symbol strings according to predefined rules. These machines provide a formal representation of how systems transition between states while handling input. The term “finite” underscores the fact that these automata have a limited number of states, distinguishing them from more intricate computational models.

Finite Automata come in two key types: deterministic and nondeterministic. Deterministic Finite Automata (DFA) uniquely link each input symbol to a next state. This characteristic simplifies their comprehension and implementation. In contrast, nondeterministic finite automata (NFA) allow for multiple potential transitions from a given state for a specific input symbol, yielding diverse state traversal paths.

Exploring Finite Automata entails grasping several crucial concepts:

  1. States: Represent distinct stages or configurations attainable during automaton operation. One state is designated as the starting point, while others can be marked as final states.
  2. Transitions: Dictate movement between states based on input symbols. Each state-input combination is tied to a defined transition leading to another state.
  3. Alphabet: Denotes the set of symbols that the automaton processes from the input. This forms the basic vocabulary the automaton understands.
  4. Input Strings: Finite Automata handle input strings composed of alphabet symbols. These strings guide state transitions.
  5. Acceptance: The automaton’s behavior, for a given input, is assessed to determine whether it reaches an accepting state. Acceptance indicates that the input string conforms to the automaton’s recognized language.
  6. Language Recognition: The core purpose of a Finite Automaton is to ascertain whether an input string belongs to a specified language. A language consists of strings defined over the alphabet.

Finite Automata provide a structured and formal approach to assessing diverse system computational abilities, algorithm efficiency, and computation limits. Their simplicity and practicality establish them as a cornerstone within computer science education and research. Mastering Finite Automata fosters comprehension of essential computational principles and lays the groundwork for understanding intricate computational models and ideas.

Example: Perhaps the simplest nontrivial finite automaton is an on/off switch. The device remembers whether it is in the “on” state or the “off” state, and it allows the user to press a button whose effect is different, depending on the state of the switch. That is, if the switch is in the off state, then pressing the button changes it to the on state, and if the switch is in the on state, then pressing the same button turns it to the off state.

A finite automaton modeling an onoff switch
Fig-1: A finite automaton modeling an on-off switch

Imagine we have a special diagram that looks like a bunch of circles connected by lines. Each circle represents a different situation our system can be in. For our example, let’s say our system is a switch, and the situations are “on” and “off.”

Now, imagine we have a button, and every time we press it, something happens to the switch. This button pressing is like an “input” that affects our system.

In our diagram, we draw lines between the circles and label them with the word “Push,” which is like saying “someone pushed the button.” No matter if our switch is “on” or “off,” when we get a “Push” input, the switch goes to the other state – if it’s “on,” it goes “off,” and if it’s “off,” it goes “on.”

There’s one special circle that we start with; it’s like the beginning of our story. In this case, it’s the “off” state. We show this by putting the word “Start” and an arrow pointing to that circle.

Now, sometimes we want to show that something good happened. We use a double circle for that. For example, if the switch is “on,” it means the thing it’s controlling is working. So, we could put a double circle on the “on” circle to show that it’s a good state.

In the end, this diagram helps us understand how our switch behaves when we press the button. It’s like a fun map that shows us all the different situations our switch can be in, and how it changes when we do something.

Example-2: Sometimes, what is remembered by a state can be much more complex than an on/off choice. Figure 2 shows another finite automaton that could be part of a lexical analyzer. The job of this automaton is to recognize the keyword then. It thus needs five states, each of which represents a different position in the word then that has been reached so far. These positions correspond to the prefixes of the word, ranging from the empty string (i.e., nothing of the word has been seen so far) to the complete word.

A finite automaton modeling recognition of then
Fig-2: A finite automaton modeling recognition of then

In Figure 2, you’ll see five states, and they’re named after the parts of a scene that have been seen so far. Imagine each state as a checkpoint in a story. The inputs, which are like letters, are what guide us through the story. Think of the process like this: a detective, which we can call the “lexical analyzer,” reads one letter at a time from the program it’s trying to understand. The very next letter to be read becomes the input for the automaton.

At the very beginning, we’re at what we call the “start” point. It’s like the beginning of the story where nothing has happened yet. From there, each state moves to another state based on the next letter. For instance, if the next letter is “t,” we move to the state that’s like the part of the story starting with “t.” It’s like we’re uncovering the story step by step.

Now, let’s say we have a state called “then.” This is the state we enter when the input has spelled out the word “then.” Since our goal is to figure out when the word “then” appears, we could say that this state is the one we’re looking for – like the detective finding a clue. In fact, we could think of this state as the special “accepting” state because it’s the one that tells us we’ve found what we were looking for in the story.

So, in a nutshell, the diagram in Figure 2 is like a map of a story with checkpoints that match up to what we’ve read so far. And as the detective (the lexical analyzer) reads through the program, it’s like the story is unfolding before us, with the automaton guiding us to the interesting parts, like when we find the word “then.”

Structural Representations

There are two important notations that are not automaton-like, but play an important role in the study of automata and their applications.

  • Grammars are useful models when designing software that processes data with a recursive structure. The best-known example is a “parser,” the component of a compiler that deals with the recursively nested features of the typical programming language, such as expressions — arithmetic, conditional, and so on. For instance, a grammatical rule like E => E+E states that an expression can be formed by taking any two expressions and connecting them by a plus sign; this rule is typical of how expressions of real programming languages are formed.
  • Regular Expressions also denote the structure of data, especially text strings. The style of these expressions differs significantly from that of grammars, and we shall content ourselves with a simple example here. The UNIX-style regular expression ’ [A~Z] [a-z]*[ ] [A-Z] [A-Z]’ represents capitalized words followed by a space and two capital letters. This expression represents patterns in text that could be a city and state, e.g., Ithaca NY. It misses multiword city names, such as Palo Alto CA, which could be captured by the more complex expression

‘([A-Z][a-z]*[ ] )* [ ] [A-Z][A-Z]’

When interpreting such expressions, we only need to know that [A-Z] represents a range of characters from capital “A” to capital “Z” (i-e., any capital letter), and [ ] is used to represent the blank character alone. Also, the symbol * represents “any number of” the preceding expression. Parentheses are used to group components of the expression; they do not represent characters of the text described.

Automata and Complexity

Automata are essential for the study of the limits of computation. As we mentioned in the introduction to the chapter, there are two important issues:

  • What can a computer do at all? This study is called “decidability,” and the problems that can be solved by computer are called “decidable.”
  • What can a computer do efficiently? This study is called “intractability,” and the problems that can be solved by a computer using no more time than some slowly growing function of the size of the input are called “tractable.” Often, we take all polynomial functions to be “slowly growing,” while functions that grow faster than any polynomial are deemed to grow too fast.

Introduction to Formal Proof

Back before the 1990s, when you were learning about shapes and measurements in high school geometry class, you might remember doing something called “deductive proofs.” These were like detailed explanations that showed why statements about angles, lines, and shapes were true. You had to follow a sequence of logical steps and give reasons for each step. These proofs weren’t just about math; they were about teaching you to think logically and explain things in a structured way. Geometry had practical uses too, like helping you figure out how much carpet you needed for a room based on its shape.

Shift in Teaching Proofs and its Impact:

Around the 1990s in the US, the way proofs were taught changed. Instead of focusing so much on the detailed logical steps, there was more emphasis on understanding and feeling the truth of a statement. This meant that instead of showing every single step, you might be encouraged to “feel” that a statement is true without necessarily proving it formally.

Proofs in Computer Science and Differing Views:

Now, let’s talk about computer scientists – the people who create computer programs. Some computer scientists believe that proving a program’s correctness is really important. They think that besides writing the program, you should also provide a formal proof that the program does what it’s supposed to do. However, there are differing opinions. Some think that proving isn’t necessary and that testing the program is enough. They say, “If you’re not sure your program is correct, run it and see.”

Finding a Balanced Approach:

Most people land somewhere in between these two extremes. They understand the value of testing programs – like trying them out with different inputs to make sure they work as intended. However, testing has limits. You can’t test every single situation, especially when programs get complex.

Understanding Complexity and Using Inductive Hypotheses:

When dealing with complex programs that involve loops or repeated steps, simply testing them might not be sufficient. You need to understand how those repeating parts work. Imagine trying to fix a car without knowing how the engine works – you might make mistakes. To make sure these complex parts of your program are correct, you use something called an “inductive hypothesis.” This is like making an educated guess about what’s happening during those repeating steps.

Connection to Geometry Proofs and Automata Theory:

This process of deeply understanding how complex programs work is similar to the way you used to prove things in geometry class. Just as you provided reasons step by step, here you’re making informed assumptions about the program’s behavior during certain parts.

In the world of computer science, a topic called “automata theory” provides a solid ground for learning formal proof methods. This topic helps computer scientists understand how to prove things about their programs in a clear and organized manner. In conclusion, while there are different opinions, many believe that combining testing with deep understanding, similar to the process of geometry proofs, is essential for building reliable and effective software.

Deductive Proofs

Deductive proofs are a way of logically showing that a certain mathematical statement is true. Here’s an example to help you understand:

Example: Proving that the Sum of Even Numbers is Even

Statement: The sum of two even numbers is always an even number.

Step 1: Even Numbers
Even numbers are those that can be divided by 2 without leaving a remainder. We’ll use the variables “a” and “b” to represent even numbers.

Step 2: Expressing Even Numbers
Let’s express two even numbers as “a = 2k” and “b = 2m,” where “k” and “m” are integers.

Step 3: Sum of Even Numbers
Now, let’s add these two even numbers:
Sum = a + b = 2k + 2m.

Step 4: Factoring Out Common Factors
We can factor out 2 from the sum:
Sum = 2(k + m).

Step 5: Even Sum
Since both “k” and “m” are integers, their sum “k + m” is also an integer. Therefore, the sum of two even numbers can be expressed as 2 times an integer, which is still an even number.

Conclusion: Deductive Proof
By breaking down the process using variables, equations, and logical steps, we have shown that the sum of two even numbers (a and b) is always an even number. This deductive proof demonstrates that our statement holds true for all pairs of even numbers.

In this example, we used equations and algebraic manipulation to logically establish the truth of the statement. This process of breaking down the problem using mathematical symbols and logic is at the heart of deductive proofs in mathematics.

Proof by contradiction

Proof by Contradiction: A Simple Explanation

Proof by contradiction is a clever method in mathematics where we assume the opposite of what we want to prove, and then show that this assumption leads to a contradiction – something that doesn’t make sense. This tells us that our original assumption (the opposite of what we’re trying to prove) must be wrong, and therefore, what we actually want to prove must be true.

In More Detail:

Let’s say we want to prove that statement A is true. We start by assuming that statement A is false. Then, we use logical steps and math to show that this assumption leads to something impossible or contradictory.

For example, let’s prove that the square root of 2 is an irrational number using proof by contradiction.

Statement to Prove: √2 is irrational (cannot be expressed as a fraction).

Assumption: Let’s assume that √2 is rational (can be expressed as a fraction a/b where a and b have no common factors).

Logic and Math Steps:

  1. Assume √2 is rational: √2 = a/b (where a and b have no common factors).
  2. Square both sides: 2 = a^2 / b^2.
  3. Multiply both sides by b^2: 2b^2 = a^2.

Now, we’ve shown that if √2 is rational, then a^2 is even (since it’s twice of b^2). This means “a” must also be even (because the square of an odd number is odd).

But wait! If “a” is even, then “a^2” is also even. However, we initially assumed that “a^2” was even because √2 is rational. This is a contradiction because “a^2” cannot be both even (from our assumption) and odd (since “a” is even). Contradictions can’t exist in mathematics.

Since our assumption that √2 is rational led to a contradiction, it must be false. Therefore, √2 cannot be rational, meaning it’s irrational.

Proof by contradiction is like playing detective – we assume a scenario, then follow the clues until we find a contradiction, proving that our original assumption was wrong. This way, we can confidently conclude that the statement we wanted to prove is true.

Statements With Quantifiers

Understanding “For All” and “There Exists” in Theorems: A Simple Explanation

Imagine you’re playing a game with two players – let’s call them “For-All” and “There-Exists.” These players are actually used to talk about things in math called “quantifiers.” These quantifiers show up when we talk about statements that involve “for all” or “there exists.” The order of these players’ turns matters a lot!

The Game of Quantifiers:

  1. For-All’s Turn: When “for all” is in the lead, it means For-All has to think about all possible choices. So, For-All’s choices are often left as variables or any possibilities. They need to cover every option.
  2. There-Exists’s Turn: Now, “there exists” gets a chance. They only have to pick one value, and they can base their choice on what For-All did earlier.

Order Matters:

The order in which these players show up in a statement changes how we understand it. If For-All goes first and There-Exists follows, it’s like For-All is trying to handle every situation, and There-Exists tries to find one good option.

Example: An Infinite Set:

Think about a type of set called “infinite sets.” We want to say a set is infinite if, no matter what integer you choose (like 1, 2, 3…), there’s always a subset (a smaller group) in that set with exactly that many members.

For example, imagine we say: “For all integers n, there exists a subset T of set S with exactly n members.”

Here’s how the players play:

  • For-All starts by thinking about any integer “n.”
  • Then, There-Exists jumps in and picks a subset “T” based on what For-All thought.
  • For-All was trying to cover all integers, so they did their best.
  • There-Exists just needed to find one subset that matches what For-All was thinking.
  • If There-Exists can always find a good subset no matter what For-All chooses, then the statement is true.

Understanding the Mistake:

Now, let’s flip the order: “There exists a subset T of set S such that for all n, set T has exactly n members.”

Here’s how the game goes now:

  • There-Exists starts by picking any subset “T.”
  • For-All steps in and has to show that, no matter what subset There-Exists picked, it will have exactly “n” members for every possible integer “n.”
  • This is where things go wrong. For-All can’t always do this.
  • Think of the example where There-Exists picked the subset {1, 2, 5}. For n = 4 or any number not equal to 3, For-All can’t find a subset with exactly that number of members.


The order of “For All” and “There Exists” matters because it changes who needs to cover all possibilities and who only needs to find one good choice. Getting the order wrong can lead to mistakes in math statements.


If-Then Statements in Math: A Simple Explanation with an Example

“If-then” statements in math are like telling a story where one thing leads to another. We say that if a certain condition is true (the “if” part), then something else will definitely be true (the “then” part). Let’s see this with a simple equation example:

Statement: If a number is greater than 5, then its square is also greater than 25.

In this statement, we have an “if-then” setup. We’re saying that if we have a number that’s bigger than 5, then when we square that number, the result will be greater than 25.

Math Equation Example:
Let’s use the number 6 as our example.

If Part: We’re saying “If the number is greater than 5.” For our example, 6 is indeed greater than 5.

Then Part: Now, we need to show that “the square is greater than 25.” Let’s square 6: 6^2 = 36. And yes, 36 is indeed greater than 25.

Conclusion: Our “if-then” statement holds true for this example. When we started with a number (6) that’s greater than 5, and we squared it, the result (36) was indeed greater than 25.

Generalizing the Rule: We can see that this works for any number greater than 5. If we start with a number that’s bigger than 5 and square it, the result will always be greater than 25.

In math, “if-then” statements help us see how things relate. We’re making a rule that says “if” a certain thing is true, “then” something else will also be true. It’s like following a logical path where one idea leads us to another, helping us understand the connections between numbers and their properties.

Proving Equivalences About Sets

Proving Equivalences About Sets: A Simple Explanation

Proving equivalences about sets means showing that two different statements or properties related to sets are actually saying the same thing. It’s like showing that two roads lead to the same destination. Let’s break it down in an easy way:

Equivalences in Sets – Explained:

When we talk about sets (collections of things), we often want to prove that two statements are basically just two different ways of saying the same thing. It’s like proving that “apples are fruits” is the same as saying “fruits are apples.” Both statements mean the same idea.

How It Works:

  1. Starting with Two Statements: Imagine you have two statements, A and B, related to sets. These could be about membership, elements, or properties of sets.
  2. Proving the Equivalence: To prove that A and B are equivalent, you need to show that if one is true, the other is true, and if one is false, the other is false. It’s like showing that both roads lead to the same spot.

Let’s say we want to prove that “The set A is a subset of set B” is equivalent to “Every element in set A is also in set B.”

  1. Statement A: The set A is a subset of set B.
  2. Statement B: Every element in set A is also in set B.

Proving the Equivalence:
We need to show that if one statement is true, the other is true, and vice versa.

  • If A is a subset of B (A ⊆ B), it means that every element in A is also in B. So, Statement A implies Statement B.
  • If every element in A is also in B, it means that A is a subset of B. So, Statement B implies Statement A.

Since both sides lead to the same conclusion, we’ve proved that “A is a subset of B” is equivalent to “Every element in A is also in B.”


Proving equivalences about sets helps us understand that different statements about sets are essentially saying the same thing. It’s about showing that you can take two different paths to reach the same understanding. Just like knowing that “apples are fruits” and “fruits are apples” is essentially the same idea, proving equivalences in sets helps mathematicians make connections and simplify their reasoning about sets and their properties.

The Contrapositive

The Contrapositive: A Simple Explanation with an Example

The contrapositive is like looking at a situation from a different angle to see if it still makes sense. It’s a clever way to understand connections between statements. Let’s break it down:

Understanding the Contrapositive:

Imagine you’re trying to figure out if something is true. The contrapositive helps you see if the opposite situation is also true.

Let’s say you’re in charge of a treasure chest and you’ve put a lock on it. You want to make sure no one can open it without the right key. Now, imagine someone tells you, “If the lock is not opened by the correct key, then the treasure chest won’t be revealed.”

Contrapositive – Flipping the Situation:

The contrapositive says, “If the treasure chest is revealed, then the lock was opened by the correct key.”

See what happened there? We flipped the situation around. Instead of talking about the negative side (not opened by correct key), we’re now focusing on the positive side (treasure chest revealed).

In Math Terms:

In math, we use the contrapositive to understand connections between statements. If we have a statement like “If A, then B,” the contrapositive flips it to “If not B, then not A.”

Example in Math:
Statement: “If a number is even, then its square is even.”
Contrapositive: “If the square is not even, then the number is not even.”

Here’s how it works:

  • Original Statement: If a number is even, then its square is even.
  • Contrapositive: If a square is not even (which means it’s odd), then the number is not even.


The contrapositive helps us explore relationships between statements by looking at the opposite sides. It’s like checking if the treasure chest can only be opened with the right key. In math, the contrapositive helps us understand how different statements are linked and how one thing follows from another. It’s like turning the puzzle piece around to see how it fits from a new perspective.

The Contrapositive in Sets: A Simple Set Example

Let’s dive into a set example to understand the contrapositive in action:

Statement: “If a number is not in set A, then it’s not in set B.”
Contrapositive: “If a number is in set B, then it’s not in set A.”

Explaining with Sets:

Imagine we have two sets, set A and set B. We’re talking about numbers that might or might not be in these sets.

Original Statement:
“If a number is not in set A, then it’s not in set B.”
This means that if a number isn’t part of set A, then it definitely won’t be part of set B.

Contrapositive – Flipping the Focus:
Now, let’s look at the contrapositive:
“If a number is in set B, then it’s not in set A.”
Here, we’re focusing on numbers that are in set B and saying that they won’t be in set A.

Let’s say we have set A = {1, 2, 3} and set B = {3, 4, 5}.

Original Statement:
“If a number is not in set A, then it’s not in set B.”
If a number is not in set A (like 4 or 5), then it’s not in set B (which is true in this case).

“If a number is in set B, then it’s not in set A.”
If a number is in set B (like 3), then it’s not in set A (which is true in this case).


In this example, we explored the original statement and its contrapositive using sets. The contrapositive helped us see that if a number is in one set, it can’t be in the other. The contrapositive technique helps us understand connections and relationships between statements, whether in sets, numbers, or other situations. It’s all about looking at things from a different angle to get a clearer picture.


Counterexamples in Sets: A Simple Set Example

Counterexamples are like showing exceptions to a rule. They help us understand when something doesn’t work as expected. Let’s use a set example to see how counterexamples work:

Statement: “All birds can fly.”

Counterexample: Penguins are birds but cannot fly.

Understanding Counterexamples:

When we make a general statement, like “All birds can fly,” a counterexample is a specific case that shows the statement is not always true.

Example in Sets:

Let’s say we’re trying to understand the statement “All prime numbers are even.”

Counterexample: The number 3 is a prime number, but it’s not even.


  1. Statement: “All prime numbers are even.” This is our initial thought, but let’s test it.
  2. Counterexample: The number 3 is a prime number, but it’s not even. It contradicts our statement.


The counterexample of 3 shows that our initial statement “All prime numbers are even” is not true. While some prime numbers are indeed even (like 2), not all of them are (like 3). The counterexample helps us see the limits of our statement and understand that it doesn’t hold in all cases.

In math, counterexamples play a crucial role in testing the validity of statements and theories. They help us refine our understanding, identify exceptions, and ensure that our generalizations are accurate.


Inductive Proofs in Sets: A Simple Set Example

Inductive proofs are like building a strong chain of logic, link by link. They help us prove statements for all cases by starting small and showing how each case leads to the next. Let’s understand inductive proofs with a set example:

Statement to Prove: “The sum of the first n positive integers is n(n+1)/2.”

Inductive Proof Steps:

  1. Base Case: Start with the simplest case, often the smallest value of n, which is 1 in this case. For n = 1: The sum of the first positive integer is 1. On the other side, n(n+1)/2 = 1(1+1)/2 = 1. So, the statement holds for n = 1.
  2. Assumption: Assume that the statement is true for some value of n, which we’ll call k. Assumption: The sum of the first k positive integers is k(k+1)/2.
  3. Inductive Step: Now, we want to show that if the statement holds for k, it also holds for k+1. For n = k+1: The sum of the first k+1 positive integers is 1 + 2 + … + k + (k+1). Using Assumption: According to our assumption, the sum of the first k positive integers is k(k+1)/2. So, the sum of the first k+1 positive integers becomes k(k+1)/2 + (k+1). Simplifying: Factor out (k+1): (k+1)[k/2 + 1] = (k+1)(k+2)/2. Now, this is exactly what we wanted to prove for n = k+1.
  4. Conclusion: By showing that if the statement holds for k, it also holds for k+1, we’ve proved the statement using induction.

Understanding the Example:

Inductive proofs are like a staircase – we start with the first step (base case), assume each step is solid (assumption), and show that if one step is good, the next one will be too (inductive step). This lets us build a strong case for the entire staircase (all positive integers).

In this set example, we started with a simple case (n=1), assumed the statement held for some value (n=k), and showed that if it held for k, it also held for k+1. This process builds our confidence that the statement is true for all positive integers.

Deductive vs. Inductive Reasoning: A Clear Comparison

Deductive Reasoning in Math:
Deductive reasoning in math involves starting with known mathematical truths or axioms and using logical steps to reach specific conclusions. It’s like following a logical path from general principles to particular outcomes.

Premise (known truth): All right angles measure 90 degrees.
Step 1: If angle A is a right angle, then its measure is 90 degrees.
Step 2: Angle B is a right angle.
Conclusion: Therefore, angle B measures 90 degrees.

In this deductive process, we used the known premise about right angles to logically conclude the measurement of angle B.

Inductive Reasoning in Math:
Inductive reasoning in math involves drawing general conclusions based on specific examples or patterns. It’s like making a prediction about a broader mathematical principle based on observed instances.

Observation: The first five even numbers are 2, 4, 6, 8, and 10.
Pattern: Each even number is 2 more than the previous one.
Conclusion: All even numbers are likely to follow the pattern of being 2 more than the previous even number.

In this inductive process, we observed a pattern in specific even numbers and then generalized it to all even numbers, even though we didn’t check every single even number.

Deductive reasoning relies on existing mathematical truths to draw specific conclusions, while inductive reasoning uses observed patterns to make general predictions. Deductive reasoning is more certain and formal, while inductive reasoning is based on probability and observed trends. Both types of reasoning play important roles in mathematics, helping us build and apply mathematical knowledge.

The Central Concepts of Automata Theory

Let’s explore the fundamental ideas of alphabet, strings, and language in automata theory through a simple example:


An alphabet is a finite, nonempty set of symbols. Conventionally, we use the symbol Σ for an alphabet. Common alphabets include:

  • Σ= {0,1}, the binary alphabet.
  • Σ= {a,b,…,z}, the set of all lower-case letters.
  • The set of all ASCII characters, or the set of all printable ASCII characters.


A string (or sometimes word) is a finite sequence of symbols chosen from some alphabet. For example, 01101 is a string from the binary alphabet Σ= {0,1}. The string 111 is another string chosen from this alphabet.

The Empty String

The empty string is the string with zero occurrences of symbols. This string, denoted ε, is a string that may be chosen from any alphabet whatsoever.

Length of a String

It is often useful to classify strings by their length, that is, the number of positions for symbols in the string. For instance, 01101 has length 5. It is common to say that the length of a string is “the number of symbols” in the string; this statement is colloquially accepted but not strictly correct. Thus, there are only two symbols, 0 and 1, in the string 01101, but there are five positions for symbols, and its length is 5. However, you should generally expect that “the number of symbols” can be used when “number of positions” is meant.

The standard notation for the length of a string w is |w|. For example, [011| = 3 and |ε| = 0.

Powers of an Alphabet

If = is an alphabet, we can express the set of all strings of a certain length from that alphabet by using an exponential notation. We define Σk to be the set of strings of length k, each of whose symbols is in Σ.

Example: Note that Σ0 = {e}, regardless of what alphabet Σ is. That is, ε is the only string whose length is 0.

If Σ= {0,1}, then Σ1 = {0,1}, Σ2 = {00,01, 10, 11},

Σ3 = {000, 001, 010, 011, 100, 101, 110, 111}

and so on. Note that there is a slight confusion between Σ and Σ1. The former is an alphabet; its members 0 and 1 are symbols. The latter is a set of strings; its members are the strings 0 and 1, each of which is of length 1. We shall not try to use separate notations for the two sets, relying on context to make it clear whether {0,1} or similar sets are alphabets or sets of strings.

The set of all strings over an alphabet = Σ is conventionally denoted Σ*. For instance, {0,1}* = {ε ,0,1,00,01, 10, 11,000,…}. Put another way,

Σ*=Σ0 ∪  Σ1 ∪  Σ2 ∪ …….

Sometimes, we wish to exclude the empty string from the set of strings. The set of nonempty strings from alphabet Σ is denoted Σ+. Thus, two appropriate equivalences are:

  • Σ+ = Σ1 ∪ Σ2 ∪ Σ3 ∪ ……
  • Σ* = Σ0 ∪ Σ1 ∪ Σ2 ∪ Σ3 ∪ ……

Concatenation of Strings

The concept of concatenation refers to combining strings within the context of formal languages. Concatenation is a fundamental operation used to create new strings by joining two or more existing strings together.

In formal language theory, the concatenation of two strings is typically denoted by the juxtaposition [ Juxtaposition is a term used to describe the act of placing two or more things together ] of the strings without any operator symbol. Let’s look at an example:

Let Σ be an alphabet containing the symbols {a, b}, and consider the language L over Σ defined as follows:
L = {a, aa, aaa, b, bb, bbb}

Here, L is a set of strings formed using the symbols in Σ. Now, let’s perform concatenation operations on some of these strings:

  1. Concatenation of “a” and “b”:
    “a” concatenated with “b” results in the string “ab”.
  2. Concatenation of “aa” and “bbb”:
    “aa” concatenated with “bbb” results in the string “aabb”.
  3. Concatenation of “b” and “aa”:
    “b” concatenated with “aa” results in the string “baa”.

In automata, concatenation is important because it helps define how strings are constructed within a formal language. Automata, such as finite automata and pushdown automata, can be used to recognize and generate strings based on certain rules. Concatenation often plays a role in defining the transition rules of these automata.

For instance, in a finite automaton, the transition from one state to another might be triggered by reading a certain symbol, and the automaton can move from state to state as symbols are read. Concatenation of strings essentially represents the process of transitioning from one state to another in the automaton.

Set-Formers as a Way to Define Languages

Set-builder notation is another way to define languages in the context of automata theory. Set-builder notation provides a concise and mathematical way to define a set of strings based on specific criteria or rules. In the realm of formal languages and automata theory, set-builder notation is often used to define languages in a more general and abstract manner. Here’s how set-builder notation works in this context:

In set-builder notation, you define a set by specifying a variable, a condition, and the domain from which the variable is chosen. The variable represents the strings you’re considering, and the condition specifies the criteria for membership in the set. Here’s the general format:

{ variable | condition }

For example, if you want to define a language that contains all binary strings with an equal number of 0s and 1s, you can use set-builder notation as follows:

L = { w | w is a binary string with an equal number of 0s and 1s }

In this example, the variable “w” represents a binary string, and the condition specifies that the string should have an equal number of 0s and 1s. This set-builder notation defines the language L, which contains strings like “0011”, “0101”, “1100”, and so on.

Set-builder notation provides a powerful and flexible way to define languages based on various criteria or properties. It’s commonly used when describing formal languages, which can then be recognized or generated by different types of automata, such as finite automata, pushdown automata, or Turing machines.

  • {0n1n | m > 1}. Read “the set of 0 to the n 1 to the n such that n is greater than or equal to 1,” this language consists of the strings {01,0011,000111,…}. Notice that, as with alphabets, we can raise a single symbol to a power n in order to represent n copies of that symbol.
  • {0i1j |0 <i <j}. This language consists of strings with some 0’s (possibly none) followed by at least as many 1’s.

Problems in Automata

Consider the problem of determining whether a given string is a palindrome. A palindrome is a string that reads the same forwards as it does backwards. For instance, “radar” and “level” are palindromes, while “apple” and “banana” are not.

Let’s denote the language of palindromes over the binary alphabet {0, 1} as (P = { w \mid w \text{ is a palindrome} }). Now, we have the problem (P) which is the task of deciding whether a given string is a palindrome.

Here’s how we can frame this problem:

Problem (P): Given a string (w) over the alphabet {0, 1}, determine whether (w) is a palindrome.

For example, let’s take the string “101101”. To solve problem (P) for this string, we need to check whether “101101” is a palindrome:

  1. Compare the first and last characters: “1” matches “1”.
  2. Move to the second characters from the beginning and end: “0” matches “0”.
  3. Third characters: “1” matches “1”.
  4. Fourth characters: “1” matches “1”.
  5. Fifth characters: “0” matches “0”.
  6. Sixth characters: “1” matches “1”.

Since every character matches its counterpart on the other side, “101101” is indeed a palindrome. Thus, the answer to problem (P) for the input “101101” is “Yes, it’s a palindrome.”

In this complex example, the problem involves determining whether a string adheres to a specific linguistic property (being a palindrome) defined by the language (P). Automata theory provides a structured way to analyze and solve such problems, often involving the design and analysis of automata that recognize or generate the language in question. Solving these problems can involve both theoretical reasoning and algorithmic approaches to determine whether a given string belongs to a language or satisfies a certain property.

Reference: A. V. Aho and J. D. Ullman, Foundations of Computer Science, Computer
Science Press, New York, 1994.

Leave a Comment