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Una tautología: A OR NOT A, comprobada en todas las filas

A OR NOT A es verdadero en las dos filas de su tabla. La comprobación de tautología se cumple, el simplificador lo reduce a TRUE por la ley del complemento, y las dos formas normales se contraen a TRUE.

Una tautología es una fórmula verdadera en todas las filas de su tabla de verdad, sean cuales sean las entradas. La ley del tercero excluido, A OR NOT A, es la más sencilla: o se cumple A o se cumple su negación. La lectura del motor dice que always es verdadero exactamente cuando A es verdadero o A es falso, o ambas cosas, que es todos los casos. Con una sola variable la tabla tiene dos filas, y la columna de salida es verdadera en las dos, Σm(0, 1); la lista de maxtérminos está vacía.

La tautología se cumple: always es verdadero en todas las asignaciones. La contradicción se viola y la satisfacibilidad se cumple, ambas con el testigo A = FALSO. El simplificador aplica la ley del complemento, registrada como A OR NOT A = TRUE, y en un paso lleva la expresión de dos operadores a la constante TRUE, exactamente equivalente. El conversor a FNC devuelve TRUE como conjunción vacía, cero cláusulas, y el de FND como un término vacío, ambos por la ley del complemento. El mapa cubre las dos celdas con un grupo y deriva TRUE. Compuertas: 2 NAND, NAND(NAND(A, A), A), y 5 NOR.

Las tautologías importan en la práctica como lo que nunca debería escribirse como condición: un if cuya prueba es siempre verdadera es una bifurcación muerta, y una restricción de base de datos que es una tautología no comprueba nada. Importan en el sentido contrario como propiedades: una regla de seguridad que debe cumplirse en todos los estados es exactamente una fórmula que quieres que la comprobación de tautología confirme. Lo que hay que vigilar es el alcance. La comprobación es sobre las filas de las variables del modelo; la página del NULL de SQL muestra la misma fórmula dejando de ser siempre verdadera en cuanto entra un tercer valor.

always := A OR NOT A · salida

Comprobaciones exactas

¿Siempre verdadera (tautología)? Sí · Exhaustively checked and solver cross-checked · 2 filas
¿Siempre falsa (contradicción)? No Contraejemplo: A = F · Exhaustively checked and solver cross-checked · 1 filas
¿Puede ser verdadera? Sí Asignación de ejemplo: A = F · Exhaustively checked and solver cross-checked · 1 filas

Tabla de verdad (2 filas)

Una tautología: A OR NOT A, comprobada en todas las filas
#Aalways
0FALSOVERDADERO
1VERDADEROVERDADERO

Lectura en lenguaje claro

Principiantealways es verdadero exactamente cuando A es verdadero o A es falso (o ambas cosas).
En todos los demás casos, always es falso.
Sobre los operadores:
- OR (O) es inclusivo: es verdadero en cuanto al menos una parte es verdadera, también cuando lo son varias.
- NOT (NO) invierte un valor: lo verdadero pasa a ser falso y lo falso, verdadero.
Intermedioalways es verdadero exactamente cuando A es verdadero o A es falso (o ambas cosas).
En todos los demás casos, always es falso.
Avanzadoalways es verdadero exactamente cuando A es verdadero o A es falso (o ambas cosas) [disyunción inclusiva ∨].
En todos los demás casos, always es falso.
Notación formal: A ∨ ¬A

Diagrama lógico

A tautology: A OR NOT A, checked on every row A tautology: A OR NOT A, checked on every row A always OR NOT
A tautology: A OR NOT A, checked on every row A tautology: A OR NOT A, checked on every row A always OR NOT

Simplificación paso a paso

A OR NOT A
  1. Complemento · A OR NOT A = TRUE
    A OR NOT A → TRUEResultado: TRUE
TRUE · equivalencia verificada en todas las filas · simplificado, no mínimo

Mapa de Karnaugh

A
0
1
1m0
1m1
  1. celdas m0, m1TRUE
TRUE · Derived TRUE. Exactly equivalent on every assignment.

Mintérminos y maxtérminos

always(A) = Σm(0, 1)always(A) = ΠM()

Orden de variables: A · 2 filas

Formas normales

Forma normal conjuntiva (FNC)

TRUE

0 cláusulas, 0 literales · complete · Complemento ×1

Forma normal disyuntiva (FND)

TRUE

1 términos, 0 literales · complete · Complemento ×1

Solo NAND, solo NOR

Forma solo con NAND

NAND(NAND(A, A), A)

2 compuertas · equivalencia verificada en todas las filas

Forma solo con NOR

NOR(NOR(A, NOR(A, A)), NOR(A, NOR(A, A)))

5 compuertas · equivalencia verificada en todas las filas

La misma regla como código

Python
# -*- coding: utf-8 -*-
# Generated by Tangibool (tangibool.python/1). Do not edit by hand; regenerate from the source revision.
# Model: not stated
# Source revision: reference
# Interpretation status: not stated
"""Tangibool evaluator (decision model).

Generated deterministically from the validated Boolean model; it evaluates exactly that
model. It does not establish that the model is a complete, legal, or safe statement of a
real-world rule, and evaluating examples does not verify the rule.

Source revision: reference
Interpretation status: not stated
Exporter: 1.0.0 (format tangibool.python/1; engine 1.0.0; semantics tangibool-semantics/1)
Logic digest: 353e6c838b690bf7fbdf845e70083ee7c75dad7ce4830868fbb7647ada0db024
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

Inputs (dict keys, declared order) and generated parameter names:
    'A' -> input_A

Outputs (declared order) and their functions:
    'always' -> output_always(inputs)

Assumptions (the declared scope, checked by in_scope(inputs)):
    none; every complete assignment is in scope.

Input contract:
    Every required input must be exactly True or False (type(value) is bool). Missing
    inputs, None, strings, integers (including 0 and 1), and keys that are not declared
    inputs raise InputError before anything is evaluated, including inputs that a
    short-circuiting operator would never read. Results are always bool.

Scope:
    Decision functions raise OutOfScopeError when the inputs violate an assumption; an
    out-of-scope assignment is never turned into a False result. Pass check_scope=False
    to evaluate without the scope check, and use in_scope(inputs) to test it separately.

Usage:

    import tangibool_model as model

    model.evaluate({'A': False})
    # -> {'always': True}
    model.output_always({'A': False})
    # -> True
    model.evaluate({'A': 1})
    # raises InputError: 1 is an int, not a bool
"""

import collections.abc as _abc
from typing import Dict, Mapping, Tuple

__all__ = [
    "MODEL_INFO",
    "IDENTIFIER_MAP",
    "INPUT_NAMES",
    "OUTPUT_NAMES",
    "ASSUMPTIONS",
    "InputError",
    "OutOfScopeError",
    "minterm_index",
    "in_scope",
    "evaluate",
    "output_always",
]

MODEL_INFO = {
    "format": "tangibool.python/1",
    "mode": "decision",
    "model": None,
    "revision": "reference",
    "interpretation": None,
    "exporter": "1.0.0",
    "engine_version": "1.0.0",
    "semantics_version": "tangibool-semantics/1",
    "logic_digest": "353e6c838b690bf7fbdf845e70083ee7c75dad7ce4830868fbb7647ada0db024",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "always",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_3cf7e5e53a24": {"kind": "input", "name": "A", "identifier": "input_A"},
    "f_d371ed8ad9e7": {"kind": "output", "name": "always", "identifier": "output_always"},
}

INPUT_NAMES = ("A",)
OUTPUT_NAMES = ("always",)
ASSUMPTIONS = ()

_DECLARED_INPUTS = frozenset(INPUT_NAMES)
_SCOPE_INPUTS = ()
_ASSUMPTION_FORMULAS = {entry["name"]: entry["formula"] for entry in ASSUMPTIONS}


class InputError(ValueError):
    """Raised when inputs are missing, undeclared, or not exactly True/False.

    Every required input must satisfy type(value) is bool. Missing inputs, None, strings,
    integers (including 0 and 1), and keys that are not declared inputs are rejected before
    anything is evaluated. A missing input is never treated as False. The individual problems
    are listed in the problems attribute.
    """

    def __init__(self, problems):
        self.problems = tuple(problems)
        super().__init__("Invalid inputs: " + "; ".join(self.problems))


class OutOfScopeError(ValueError):
    """Raised when the inputs violate the model's assumptions (its declared scope).

    Outside its assumptions the rule has no decision: this error is never a False result.
    The names of the violated assumptions are in the violated attribute.
    """

    def __init__(self, violated):
        self.violated = tuple(violated)
        details = ", ".join(name + " (" + _ASSUMPTION_FORMULAS.get(name, "?") + ")" for name in self.violated)
        super().__init__(
            "Out of scope: the inputs violate "
            + ("assumption " if len(self.violated) == 1 else "assumptions ")
            + details
            + ". The rule has no decision outside its assumptions; this is not a False result."
        )


def _short_repr(value: object) -> str:
    text = repr(value)
    return text if len(text) <= 60 else text[:57] + "..."


def _checked_inputs(inputs: Mapping[str, bool], required: Tuple[str, ...], check_scope: bool) -> Dict[str, bool]:
    """Validate inputs strictly before anything is evaluated and return a plain copy."""
    if type(check_scope) is not bool:
        raise TypeError("check_scope must be True or False")
    if not isinstance(inputs, _abc.Mapping):
        raise InputError(["inputs must be a mapping from input names to True/False, got " + type(inputs).__name__])
    problems = []
    for key in inputs:
        if type(key) is not str or key not in _DECLARED_INPUTS:
            declared = ", ".join(repr(name) for name in INPUT_NAMES) or "none"
            problems.append("undeclared input " + _short_repr(key) + " (declared inputs: " + declared + ")")
    needed = tuple(required) + (_SCOPE_INPUTS if check_scope else ())
    for name in needed:
        if name not in inputs:
            problems.append("missing required input " + repr(name))
    for key, value in inputs.items():
        if type(key) is str and key in _DECLARED_INPUTS and type(value) is not bool:
            problems.append(
                "input " + repr(key) + " must be True or False (type bool), got "
                + type(value).__name__ + " " + _short_repr(value)
            )
    if problems:
        raise InputError(list(dict.fromkeys(problems)))
    return dict(inputs.items())


def _require_bool_args(function: str, pairs: Tuple[Tuple[str, object], ...]) -> None:
    problems = [
        "argument " + repr(name) + " of " + function + " must be True or False (type bool), got "
        + type(value).__name__ + " " + _short_repr(value)
        for name, value in pairs
        if type(value) is not bool
    ]
    if problems:
        raise InputError(problems)


def minterm_index(*values: bool) -> int:
    """Row index of an assignment over a declared variable order.

    The first value is the most significant bit, so all-False is row 0. Used for indexed
    minterm (sum) and maxterm (product) forms.
    """
    index = 0
    for value in values:
        if type(value) is not bool:
            raise InputError(["minterm_index() takes True/False values, got " + type(value).__name__ + " " + _short_repr(value)])
        index = index * 2 + (1 if value else 0)
    return index


def _scope_values(values: Dict[str, bool]) -> Tuple[Tuple[str, bool], ...]:
    """Value of every enabled, confirmed assumption for validated inputs, in declared order."""
    return ()


def _require_in_scope(values: Dict[str, bool]) -> None:
    violated = tuple(name for name, holds in _scope_values(values) if not holds)
    if violated:
        raise OutOfScopeError(violated)


def in_scope(inputs: Mapping[str, bool]) -> bool:
    """True when the inputs satisfy every assumption (the model's declared scope).

    Requires the inputs the assumptions read; raises InputError for invalid inputs.
    """
    values = _checked_inputs(inputs, (), True)
    return all(holds for _, holds in _scope_values(values))


# ---- Formula bodies (inputs are validated by the public functions below) ----


def _compute_output_always(input_A: bool) -> bool:
    """Decision output 'always': A OR NOT A"""
    return input_A or (not input_A)


# ---- Public API ----


def output_always(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'always' (stable ID f_d371ed8ad9e7).

    'always' := A OR NOT A

    Required inputs: 'A'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("A",), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_always_value = _compute_output_always(values["A"])
    return output_always_value


def evaluate(inputs: Mapping[str, bool], *, check_scope: bool = True) -> Dict[str, bool]:
    """Evaluate every output (declared order); each output is computed once.

    Required inputs: 'A'.
    Raises InputError for missing, undeclared, or non-bool inputs, and OutOfScopeError
    when check_scope is True and the inputs violate an assumption.
    """
    values = _checked_inputs(inputs, ("A",), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_always_value = _compute_output_always(values["A"])
    return {
        "always": output_always_value,
    }
PostgreSQL
-- Tangibool PostgreSQL export (tangibool.postgresql/1)
-- Target: PostgreSQL 16 or later with typed boolean inputs; tested with PostgreSQL 18.3 (PGlite 0.5.8).
-- Not a claim of compatibility with other SQL dialects.
-- Model: not stated
-- Source revision: reference
-- Interpretation status: not stated
-- Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
-- Logic digest: 353e6c838b690bf7fbdf845e70083ee7c75dad7ce4830868fbb7647ada0db024
-- Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
-- Mode: decision
--
-- Read-only: this file contains only WITH / VALUES / SELECT statements. No DDL, DML,
-- procedures, credentials, network access, or database changes. The expressions are pure
-- Boolean expressions, so their result does not depend on SQL evaluation order.
--
-- NON-NULL INPUT PRECONDITION: NULL is not a truth value of this rule. Every fragment below
-- is defined only when every input column it reads is a non-NULL boolean. The guard template
-- (section 2) marks any NULL input as 'invalid_input' and any row that violates the
-- assumptions as 'out_of_scope', and fills output columns only for 'ok' rows; a NULL in an
-- output column of another row is an envelope marker, not a third truth value. Never wrap
-- the fragments in COALESCE(..., FALSE) or otherwise choose a meaning for unknown inputs.
-- This evaluates the supplied model; it does not show that the model is a complete, legal,
-- or safe statement of a real-world rule.
--
-- Input columns (stable ID: name -> column):
--   v_3cf7e5e53a24: A -> "in_A"
-- Output columns:
--   f_d371ed8ad9e7: always -> "out_always"
-- Assumptions (scope K; exported separately and never folded into an output):
--   none; every complete input row is in scope.

-- ----------------------------------------------------------------------------------------------
-- 1. Expression fragments (parenthesised; non-NULL boolean inputs required)
-- ----------------------------------------------------------------------------------------------
--
-- [out_always] output always (stable ID f_d371ed8ad9e7)
--   always := A OR NOT A
--   PRECONDITION: "in_A" is a non-NULL boolean.
--   ("in_A" OR (NOT "in_A"))
--

-- ----------------------------------------------------------------------------------------------
-- 2. Guard template (read-only SELECT over an inputs list)
-- ----------------------------------------------------------------------------------------------
-- Replace the example rows between the markers with your own rows, or replace the inputs
-- CTE with a SELECT that provides the boolean columns listed in the header. status is
-- 'invalid_input' when any input is NULL, 'out_of_scope' when the assumptions are false, and
-- 'ok' otherwise; output columns are filled only for 'ok' rows (NULL elsewhere is an
-- envelope marker). Assumption columns are shown for rows with complete inputs.
WITH inputs ("row_id", "in_A") AS (
  -- tangibool:example-rows:begin
  VALUES
    (1, FALSE),
    (2, TRUE),
    (3, NULL::boolean)
  -- tangibool:example-rows:end
),
checked AS (
  SELECT
    inputs.*,
    CASE
      WHEN "in_A" IS NULL THEN 'invalid_input'
      ELSE 'ok'
    END AS "status"
  FROM inputs
)
SELECT
  "row_id",
  "in_A",
  "status",
  CASE WHEN "status" = 'ok' THEN ("in_A" OR (NOT "in_A")) END AS "out_always"
FROM checked
ORDER BY "row_id";
SMT-LIB
; Tangibool SMT-LIB export (tangibool.smtlib/1)
; Profile: SMT-LIB 2.6 Boolean subset (QF_UF): Bool constants, define-fun, let, push/pop, assert, check-sat, echo.
; Tested with Z3 5.1.0 (z3-solver 5.2.0, WebAssembly build).
; Model: not stated
; Source revision: reference
; Interpretation status: not stated
; Exporter: 1.0.0 (engine 1.0.0; semantics tangibool-semantics/1)
; Logic digest: 353e6c838b690bf7fbdf845e70083ee7c75dad7ce4830868fbb7647ada0db024
; Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
; Mode: decision
;
; Reading results: each query block states what sat and unsat mean for its question. A sat
; answer comes with a witness or counterexample (ask for it with (get-model) after sat only);
; unsat settles that scoped query and is solver-checked, not an independently checked proof
; certificate; unknown settles nothing. Blocks are scoped with push/pop, so assertions never
; accumulate across questions. This checks the supplied model, not whether it is a complete,
; legal, or safe statement of a real-world rule.
;
; Symbols (stable ID: name -> symbol):
;   v_3cf7e5e53a24: A -> in_A
;   f_d371ed8ad9e7: always -> out_always

(set-option :produce-models true)
(set-info :smt-lib-version 2.6)
(set-logic QF_UF)

; ---- Inputs (declared order)
(declare-const in_A Bool) ; A

; ---- Outputs (dependencies first)
; output always (stable ID f_d371ed8ad9e7): A OR NOT A
(define-fun out_always () Bool (or in_A (not in_A)))

; ---- Assumptions (the scope K; never folded into an output)
; K: conjunction of the enabled, confirmed assumptions (true when there are none)
(define-fun K () Bool true)

; ---- Definitions only: nothing is asserted and no check is run.
; Add (assert ...) and (check-sat) inside (push 1) ... (pop 1) to ask a question.
LaTeX
always = A + \overline{A}

El error habitual

Escribir una guarda como estado == 'abierto' OR estado != 'abierto' y esperar que filtre algo. La tabla muestra la fórmula verdadera en todas las filas, y el simplificador la reduce a TRUE: la condición es una tautología y la rama se ejecuta siempre. La comprobación de tautología es la forma más rápida de cazar condiciones muertas.

Preguntas

¿Por qué la FNC aparece con cero cláusulas?

Una conjunción sin cláusulas es vacuamente verdadera, así que TRUE es la FNC vacía; simétricamente, TRUE como FND es un término sin literales, y por eso la FND informa de un término y cero literales. Las dos son la manera honesta de los conversores de escribir la constante.

Relacionado

Leyes

Operadores y compuertas

Herramientas

Véase también

Ejemplos resueltos

Generado a partir del motor de Tangibool, semántica tangibool-semantics/1, motor 1.0.0, compilación . Cada tabla, mapa, simplificación y veredicto de estas páginas lo calculó el motor de Tangibool al compilar el sitio, y cada afirmación la volvió a comprobar el mismo verificador exacto que usa la app. Los límites se indican donde aplican. © 2026 Foreranger LTD.