Saltar al contenido

La implicación como NOT A OR B, verificada

A -> B comparado con NOT A OR B: la implicación material escrita sin flecha. La equivalencia se cumple en las cuatro filas, siendo A = VERDADERO, B = FALSO la única fila falsa, y la forma solo con NAND tiene 2 compuertas.

Una implicación promete que si A se cumple, B se cumple. Se rompe exactamente en una situación, A verdadero y B falso, y se mantiene en todas las demás, incluidas las dos filas en que A es falso. Por eso NOT A OR B dice lo mismo: o la premisa falla, o la conclusión se cumple. La lectura del motor de A -> B es «si A es verdadero, entonces B es verdadero». En la tabla de cuatro filas la salida es verdadera en las filas 0, 1 y 3, Σm(0, 1, 3).

La equivalencia se cumple: implication y as_or coinciden en todas las filas. La implicación no es tautología, contraejemplo A = VERDADERO, B = FALSO, la única fila rota; es satisfacible, testigo A = FALSO, B = FALSO. El simplificador no encontró ninguna ley que acorte el único operador. El conversor aplica una eliminación de la implicación, y tanto la FNC como la FND son NOT A OR B. El mapa de Karnaugh dibuja NOT A en las celdas 0 y 1 y B en las celdas 1 y 3, dos implicantes primos, y deriva la misma expresión. Compuertas: 2 NAND, NAND(A, NAND(B, B)), frente a 5 NOR.

La mayoría de los lenguajes no tienen operador de implicación, así que NOT A OR B es como se escribe una regla como «si el pedido es internacional, hace falta un código aduanero»: not international or has_customs_code. Lo que hay que vigilar es la mitad vacua: cuando A es falso la regla se satisface sea cual sea B, así que una batería de pruebas sin pedidos internacionales no demuestra nada sobre los códigos aduaneros. Las dos filas con A = FALSO son verdaderas por defecto, y las páginas del contrarrecíproco, el recíproco y el inverso muestran qué reescrituras conservan la flecha y cuáles no.

implication := A -> B · salidaas_or := NOT A OR B · salida

Comprobaciones exactas

¿Siempre verdadera (tautología)? No Contraejemplo: A = V, B = F · Exhaustively checked and solver cross-checked · 3 filas
¿Siempre falsa (contradicción)? No Contraejemplo: A = F, B = F · Exhaustively checked and solver cross-checked · 1 filas
¿Puede ser verdadera? Sí Asignación de ejemplo: A = F, B = F · Exhaustively checked and solver cross-checked · 1 filas
¿Son equivalentes implication y as_or? Sí · Exhaustively checked and solver cross-checked · 4 filas

Tabla de verdad (4 filas)

La implicación como NOT A OR B, verificada
#ABimplicationas_or
0FALSOFALSOVERDADEROVERDADERO
1FALSOVERDADEROVERDADEROVERDADERO
2VERDADEROFALSOFALSOFALSO
3VERDADEROVERDADEROVERDADEROVERDADERO

Lectura en lenguaje claro

Principianteimplication es verdadero exactamente cuando se cumple lo siguiente: si A es verdadero, entonces B es verdadero.
En todos los demás casos, implication es falso.
Sobre los operadores:
- Una condición «si… entonces» solo restringe el caso en que se cumple su parte «si»: cuando la parte «si» es falsa, la condición se cumple sea cual sea la parte «entonces». No afirma lo recíproco.
Intermedioimplication es verdadero exactamente cuando se cumple lo siguiente: si A es verdadero, entonces B es verdadero.
En todos los demás casos, implication es falso.
Avanzadoimplication es verdadero exactamente cuando se cumple lo siguiente: si A es verdadero, entonces B es verdadero [implicación material →].
En todos los demás casos, implication es falso.
Notación formal: A → B

Diagrama lógico

Implication as NOT A OR B, verified Implication as NOT A OR B, verified A B implicationPRIMARY as_or → OR NOT
Implication as NOT A OR B, verified Implication as NOT A OR B, verified A B implicationPRIMARY as_or → OR NOT

Simplificación paso a paso

El motor no encontró ninguna ley que acorte esta expresión; ya es tan corta como sus leyes de reescritura permiten.

A -> B

Mapa de Karnaugh

A \ B
0
1
0
1m0
1m1
1
0m2
1m3
  1. celdas m0, m1NOT A
  2. celdas m1, m3B
NOT A OR B · Derived NOT A OR B. Exactly equivalent on every assignment.

Mintérminos y maxtérminos

implication(A, B) = Σm(0, 1, 3)implication(A, B) = ΠM(2)

Orden de variables: A, B · 4 filas

Formas normales

Forma normal conjuntiva (FNC)

NOT A OR B

1 cláusulas, 2 literales · complete · Eliminación de la implicación ×1

Forma normal disyuntiva (FND)

NOT A OR B

2 términos, 2 literales · complete · Eliminación de la implicación ×1

Solo NAND, solo NOR

Forma solo con NAND

NAND(A, NAND(B, B))

2 compuertas · equivalencia verificada en todas las filas

Forma solo con NOR

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

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: d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

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

Outputs (declared order) and their functions:
    'implication' -> output_implication(inputs)
    'as_or' -> output_as_or(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, 'B': False})
    # -> {'implication': True, 'as_or': True}
    model.output_implication({'A': False, 'B': False})
    # -> True
    model.evaluate({'A': 1, 'B': False})
    # 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_implication",
    "output_as_or",
]

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": "d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "implication",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_84898dbe4c1b": {"kind": "input", "name": "A", "identifier": "input_A"},
    "v_1fb5957ec72e": {"kind": "input", "name": "B", "identifier": "input_B"},
    "f_de30f61d6db8": {"kind": "output", "name": "implication", "identifier": "output_implication"},
    "f_1f67a936bd18": {"kind": "output", "name": "as_or", "identifier": "output_as_or"},
}

INPUT_NAMES = ("A", "B")
OUTPUT_NAMES = ("implication", "as_or")
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_implication(input_A: bool, input_B: bool) -> bool:
    """Decision output 'implication': A -> B"""
    return (not input_A) or input_B


def _compute_output_as_or(input_A: bool, input_B: bool) -> bool:
    """Decision output 'as_or': NOT A OR B"""
    return (not input_A) or input_B


# ---- Public API ----


def output_implication(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'implication' (stable ID f_de30f61d6db8).

    'implication' := A -> B

    Required inputs: 'A', 'B'.
    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", "B"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_implication_value = _compute_output_implication(values["A"], values["B"])
    return output_implication_value


def output_as_or(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'as_or' (stable ID f_1f67a936bd18).

    'as_or' := NOT A OR B

    Required inputs: 'A', 'B'.
    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", "B"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_as_or_value = _compute_output_as_or(values["A"], values["B"])
    return output_as_or_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', 'B'.
    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", "B"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_implication_value = _compute_output_implication(values["A"], values["B"])
    output_as_or_value = _compute_output_as_or(values["A"], values["B"])
    return {
        "implication": output_implication_value,
        "as_or": output_as_or_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: d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992
-- 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_84898dbe4c1b: A -> "in_A"
--   v_1fb5957ec72e: B -> "in_B"
-- Output columns:
--   f_de30f61d6db8: implication -> "out_implication"
--   f_1f67a936bd18: as_or -> "out_as_or"
-- 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_implication] output implication (stable ID f_de30f61d6db8)
--   implication := A -> B
--   PRECONDITION: "in_A", "in_B" are non-NULL booleans.
--   ((NOT "in_A") OR "in_B")
--
-- [out_as_or] output as_or (stable ID f_1f67a936bd18)
--   as_or := NOT A OR B
--   PRECONDITION: "in_A", "in_B" are non-NULL booleans.
--   ((NOT "in_A") OR "in_B")
--

-- ----------------------------------------------------------------------------------------------
-- 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", "in_B") AS (
  -- tangibool:example-rows:begin
  VALUES
    (1, FALSE, FALSE),
    (2, FALSE, TRUE),
    (3, TRUE, FALSE),
    (4, TRUE, TRUE),
    (5, NULL::boolean, FALSE)
  -- tangibool:example-rows:end
),
checked AS (
  SELECT
    inputs.*,
    CASE
      WHEN "in_A" IS NULL OR "in_B" IS NULL THEN 'invalid_input'
      ELSE 'ok'
    END AS "status"
  FROM inputs
)
SELECT
  "row_id",
  "in_A",
  "in_B",
  "status",
  CASE WHEN "status" = 'ok' THEN ((NOT "in_A") OR "in_B") END AS "out_implication",
  CASE WHEN "status" = 'ok' THEN ((NOT "in_A") OR "in_B") END AS "out_as_or"
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: d84ae6dfb625ed5edc014d42cf82776b8e4ffdef8bd21b74c6441d2704f99992
; 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_84898dbe4c1b: A -> in_A
;   v_1fb5957ec72e: B -> in_B
;   f_de30f61d6db8: implication -> out_implication
;   f_1f67a936bd18: as_or -> out_as_or

(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
(declare-const in_B Bool) ; B

; ---- Outputs (dependencies first)
; output implication (stable ID f_de30f61d6db8): A -> B
(define-fun out_implication () Bool (=> in_A in_B))
; output as_or (stable ID f_1f67a936bd18): NOT A OR B
(define-fun out_as_or () Bool (or (not in_A) in_B))

; ---- 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
implication = A \rightarrow B
as_or = \overline{A} + B

El error habitual

Leer A -> B como «A y B» o como «B siempre que A, y no en otro caso». La tabla tiene tres filas verdaderas, no una: con A = FALSO la implicación es verdadera sea B verdadero o falso. La única fila que la falsea es A = VERDADERO, B = FALSO, que es también el contraejemplo de la comprobación de tautología.

Preguntas

¿Por qué A -> B es verdadero cuando A es falso?

Porque una implicación solo promete algo sobre el caso en que A se cumple. Si A nunca ocurre, la promesa nunca se pone a prueba y nunca se rompe. Las filas 0 y 1 lo registran: NOT A OR B es verdadero ahí porque lo es NOT A. La página de la verdad vacua trata las consecuencias para las pruebas.

Relacionado

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.