How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Decidable, Recognizable, and Enumerable Languages: Examples and Counterexamples
1 · Prerequisites
- Construction of the Natural Numbers
- Countability and Uncountability
- Decidable, Recognizable, and Enumerable Languages
- Finite Counting, Factorials and Binomial Coefficients
- Formal Languages, Encodings, and Decision Problems
- Linear Recurrences and Rational Generating Functions
- Relations, Functions, and Quotients
- Robust Machine Models and Universal Computation
- The ZFC Axioms and the Basic Set Constructions
- Turing Machines, Configurations, and Computation
2 · Summary
These examples keep the classification page concrete. One pair of trivial recognizers dovetails to a genuine decider, a few short bit strings illustrate the machine-code validity test, and the final counterexample isolates the gap between recognizability and total halting on nonmembers.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
Recognizers for strings containing a 1 and for all-zero strings
Example
Let be the language of binary strings containing at least
one 1. Its complement consists of the all-zero strings, including the empty
word.
Facts & Assumptions
Given: The language and its complement .
By A language is decidable if and only if it and its complement are recognizable, recognizers for a language and its complement can be interleaved into a decider.
Verification
A recognizer for scans the input from left to right and accepts as soon as it sees a 1; on an all-zero input it may simply continue forever. A recognizer for scans to the end of the input and accepts exactly when every scanned symbol is 0.
On any input word, exactly one of the two recognizers from step 1.1 eventually accepts. Therefore [L1] interleaves them into a decider for .
Testing a few short binary strings for machine-code well-formedness
Example
Under the chosen code of A fixed effective binary encoding of deterministic one-tape Turing machines, compare the short words
Facts & Assumptions
Given: The three displayed binary words.
By The chosen machine coding is injective and has a total decoder, the decoder either reconstructs one coded machine or reports malformed input.
By Well-formed deterministic one-tape Turing machine descriptions form a decidable language, this decoder test decides well-formed machine descriptions.
Verification
The words 0 and 10 are malformed. The first never even begins the required positive-arity tuple code, and the second announces arity but provides no self-delimiting payload block. Therefore the decoder of [L1] rejects both.
The word 111111011011100101101100101 is the code of the tuple , so it decodes to the normalized machine with , , , and the unique transition . In particular , and the tuple has the required entries. Thus [L1] accepts it as a well-formed machine description, and [L2] classifies the three sample words exactly as claimed.
This makes the decidability test concrete: two malformed inputs are rejected and one genuine machine code is accepted.
A recognizer for {0} that loops on every nonmember
Statement refuted
Whenever a machine recognizes a language, it halts on every input.
Facts & Assumptions
Given: The one-word language .
By Decidable and recognizable languages, recognizers are required to accept the members of their language, but they may diverge on nonmembers.
By FALSE: recognizable means total halting, the statement claims that such divergence cannot occur.
Counterexample
Let accept immediately on input 0 and otherwise move right forever without entering any halting state. Then [L1] shows that recognizes exactly .
Every nonmember of makes diverge, so is not total. This contradicts the claim summarized in [L2].
Therefore is a concrete counterexample to the refuted statement.
Sources
- John Watrous, Introduction to the Theory of Computing, Lecture 17: More undecidable languages; reductions
- Jean Gallier and Jocelyn Quaintance, Introduction to the Theory of Computation: Some Notes for CIS511
- John Watrous, Introduction to the Theory of Computing, Lecture 18: Further discussion of computability