Introduction

The theory of computation provides the mathematical foundations for computer science. It answers Fundamental questions: What can be computed? What cannot? How efficiently? These answers guide the Design of algorithms, programming languages, compilers, and hardware.

An alphabet $\Sigma$ is a finite, non-empty set of symbols (e.g., $\Sigma = \{0, 1\}$).

A string (or word) over $\Sigma$ is a finite sequence of symbols from $\Sigma$. The empty string Is denoted $\varepsilon$.

Notation:

• $\Sigma^*$: The set of all strings over $\Sigma$ (including $\varepsilon$).
• $\Sigma^+$: The set of all non-empty strings over $\Sigma$.
• $|w|$: The length of string $w$. $|\varepsilon| = 0$.
• $w^R$: The reversal of $w$.
• $\Sigma^n$: Strings of length $n$ over $\Sigma$.

A language $L$ over $\Sigma$ is any subset of $\Sigma^*$. The empty language is $\emptyset$ (distinct from $\{\varepsilon\}$Which contains one string).

Operations on languages:

• Union: $L_1 \cup L_2 = \{w : w \in L_1 \mathrm{ or} w \in L_2\}$.
• Intersection: $L_1 \cap L_2 = \{w : w \in L_1 \mathrm{ and} w \in L_2\}$.
• Concatenation: $L_1 \cdot L_2 = \{w_1 w_2 : w_1 \in L_1, w_2 \in L_2\}$.
• Kleene star: $L^* = \{\varepsilon\} \cup L \cup L^2 \cup \cdots$.
• Complement: $\overline{L} = \Sigma^* \setminus L$.

Formal languages arise throughout computer science. Here are representative examples Organised by complexity class.

Finite languages (always regular):

• $L_1 = \{\mathrm{true}, \mathrm{false}\}$ — the set of Boolean literals.
• $L_2 = \{w \in \{0,1\}^* : |w| \leq 3\}$ — all binary strings of length at most 3.

Regular languages (decidable by finite automata):

• $L_3 = \{w \in \{0,1\}^* : w \mathrm{ contains} the substring 101\}$.
• $L_4 = \{w \in \{0,1\}^* : w \mathrm{ has} an even number of 1\mathrm{s}\}$.
• $L_5 = \{w \in \{0,1\}^* : w \mathrm{ interpreted} in binary is divisible by 3\}$.

Context-free but not regular:

• $L_6 = \{a^n b^n : n \geq 0\}$ — matching counts of two symbols.
• $L_7 = \{w w^R : w \in \{0,1\}^*\}$ — even-length palindromes.
• $L_8 = \{w \in \{a,b,c\}^* : n_a(w) = n_b(w)\}$ — equal numbers of aS and bS.

Decidable but not context-free:

• $L_9 = \{a^n b^n c^n : n \geq 0\}$.
• $L_{10} = \{\langle G \rangle : G \mathrm{ is} a connected undirected graph\}$.

Undecidable (Turing-recognisable):

• $A_{\mathrm{TM} = \{\langle M, w \rangle : M \mathrm{ accepts} w\}}$ — the acceptance problem.
• \mathrm{HALT_}{\mathrm{TM} = \{\langle M, w \rangle : M \mathrm{ halts} on w\}}.

Not even Turing-recognisable:

• $\overline{A_{\mathrm{TM}}}$ — the complement of the acceptance problem.

This hierarchy illustrates the central theme of the course: as we move to more expressive language Classes, the corresponding machines become more powerful, but certain properties (decidability, Closure under complement) may be lost.

An important foundational observation is that the set of all languages over $\Sigma$ is uncountable, while the set of all Turing machines (and hence the set of all decidable Languages) is countable.

Theorem 1.1. The set of all languages over a non-empty alphabet $\Sigma$ is uncountable.

Proof. The set $\Sigma^*$ is countable (enumerate strings by length, then lexicographically). The set of all languages is $\mathcal{P}(\Sigma^*)$Which is uncountable by Cantor”s theorem (since $|\mathcal{P}(S)| \gt |S|$ for any set $S$). $\blacksquare$

Theorem 1.2. The set of all Turing machines is countable.

Proof. Each TM has a finite description (its states, alphabet, and transition function). Encode This as a string over a finite alphabet. The set of all finite strings is countable. $\blacksquare$

Corollary 1.3. There exist languages that are not Turing-recognisable (in fact, uncountably Many such languages).

This follows because there are uncountably many languages but only countably many TMs. The set Of Turing-recognisable languages is a countable subset of the uncountable set of all languages.

Cantor’s diagonalisation. The classic …/1-number-and-algebra/3*proof-and-logic of Theorem 1.1 uses diagonalisation: assume the Set of all languages is countable, list them as $L_1, L_2, L_3, \ldots$And construct a language $D$ that differs from each $L_i$ on the $i$-th string. Then $D$ is not in the list — contradiction. This technique reappears in the …/1-number-and-algebra/3_proof-and-logic of undecidability of $A*{\mathrm{TM}}$ (Section 5.2).

• Confusing $\emptyset$ and $\{\varepsilon\}$. The empty language has no strings; $\{\varepsilon\}$ contains one string (the empty string).
• Assuming every infinite language is non-regular. $\Sigma^*$ is regular yet infinite.
• Confusing recognisability with decidability. A language can be Turing-recognisable but not decidable (e.g., $A_{\mathrm{TM}}$).
• Assuming closure properties carry over. Regular languages are closed under complement; Turing-recognisable languages are not.

• Compiler design: Lexical analysis uses regular expressions and finite automata; parsing uses context-free grammars.
• Protocol verification: Model checking uses finite-state machines to verify communication protocols.
• Complexity theory: Understanding which problems are decidable guides the search for efficient algorithms.
• Cryptography: Security proofs rely on hardness assumptions tied to complexity classes (e.g., NP-hardness).