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Como máximo uno de tres: exclusión mutua

Exclusión mutua, NOT ((A AND B) OR (A AND C) OR (B AND C)): como máximo una de tres entradas puede ser verdadera. De Morgan convierte la mayoría negada en tres cláusulas por pares, con el mapa y los recuentos de compuertas.

La fórmula es la negación de la mayoría: es verdadera cuando ningún par de entradas es verdadero a la vez, es decir, cuando cero o una entrada es verdadera. La lectura del motor lo dice igual: at_most_one se cumple exactamente cuando no se cumple ninguno de A AND B, A AND C, B AND C. En la tabla de ocho filas la salida es verdadera en cuatro, Σm(0, 1, 2, 4): la fila de todo falso y las tres filas con una sola entrada verdadera. A diferencia de «exactamente uno», la fila de todo falso está permitida.

La tautología se viola con el contraejemplo A = FALSO, B = VERDADERO, C = VERDADERO, y la satisfacibilidad se cumple con el testigo A = FALSO, B = FALSO, C = FALSO. El simplificador no encontró ninguna ley aplicable a los seis operadores. El conversor a FNC aplica De Morgan una vez y produce (NOT A OR NOT B) AND (NOT A OR NOT C) AND (NOT B OR NOT C), tres cláusulas de dos literales. La FND tiene tres términos de dos literales, los mismos tres grupos que dibuja el mapa de Karnaugh, tres implicantes primos. Las formas solo con NAND y solo con NOR necesitan 19 compuertas cada una.

«Como máximo uno» es la mitad de seguridad de una regla de selección: no hay dos motores moviendo el mismo eje, no se cobran dos métodos de pago, no hay dos escritores con el mismo cerrojo. La FNC se lee directamente como la lista de pares prohibidos, que es como lo enunciaría un linter o un validador de configuración. Lo que hay que vigilar es la otra mitad: «como máximo uno» no dice nada de «al menos uno», y un sistema en el que no hay nada seleccionado la satisface. Combínala con la página de «exactamente uno» cuando una selección vacía también sea un error.

at_most_one := NOT ((A AND B) OR (A AND C) OR (B AND C)) · salida

Comprobaciones exactas

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

Tabla de verdad (8 filas)

Como máximo uno de tres: exclusión mutua
#ABCat_most_one
0FALSOFALSOFALSOVERDADERO
1FALSOFALSOVERDADEROVERDADERO
2FALSOVERDADEROFALSOVERDADERO
3FALSOVERDADEROVERDADEROFALSO
4VERDADEROFALSOFALSOVERDADERO
5VERDADEROFALSOVERDADEROFALSO
6VERDADEROVERDADEROFALSOFALSO
7VERDADEROVERDADEROVERDADEROFALSO

Lectura en lenguaje claro

Principianteat_most_one es verdadero exactamente cuando no se cumple la condición siguiente:
1. se cumple al menos una de las condiciones siguientes (pueden cumplirse varias):
1.1. A y B son ambos verdaderos
1.2. A y C son ambos verdaderos
1.3. B y C son ambos verdaderos
En todos los demás casos, at_most_one es falso.
Sobre los operadores:
- NOT (NO) invierte un valor: lo verdadero pasa a ser falso y lo falso, verdadero.
- OR (O) es inclusivo: es verdadero en cuanto al menos una parte es verdadera, también cuando lo son varias.
- AND (Y) solo es verdadero cuando todas sus partes son verdaderas.
Intermedioat_most_one es verdadero exactamente cuando no se cumple la condición siguiente:
1. se cumple al menos una de las condiciones siguientes (pueden cumplirse varias):
1.1. A y B son ambos verdaderos
1.2. A y C son ambos verdaderos
1.3. B y C son ambos verdaderos
En todos los demás casos, at_most_one es falso.
Avanzadoat_most_one es verdadero exactamente cuando no se cumple la condición siguiente [negación ¬]:
1. se cumple al menos una de las condiciones siguientes (pueden cumplirse varias) [disyunción inclusiva ∨]:
1.1. A y B son ambos verdaderos [conjunción ∧]
1.2. A y C son ambos verdaderos [conjunción ∧]
1.3. B y C son ambos verdaderos [conjunción ∧]
En todos los demás casos, at_most_one es falso.
Notación formal: ¬((A ∧ B) ∨ (A ∧ C) ∨ (B ∧ C))

Diagrama lógico

At most one of three: mutual exclusion At most one of three: mutual exclusion A B C at_most_one NOT OR AND AND AND
At most one of three: mutual exclusion At most one of three: mutual exclusion A B C at_most_one NOT OR AND AND AND

Simplificación paso a paso

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

NOT ((A AND B) OR (A AND C) OR (B AND C))

Mapa de Karnaugh

A \ BC
00
01
11
10
0
1m0
1m1
0m3
1m2
1
1m4
0m5
0m7
0m6
  1. celdas m0, m1NOT A AND NOT B
  2. celdas m0, m2NOT A AND NOT C
  3. celdas m0, m4NOT B AND NOT C
(NOT A AND NOT B) OR (NOT A AND NOT C) OR (NOT B AND NOT C) · Derived (NOT A AND NOT B) OR (NOT A AND NOT C) OR (NOT B AND NOT C). Exactly equivalent on every assignment.

Mintérminos y maxtérminos

at_most_one(A, B, C) = Σm(0, 1, 2, 4)at_most_one(A, B, C) = ΠM(3, 5, 6, 7)

Orden de variables: A, B, C · 8 filas

Formas normales

Forma normal conjuntiva (FNC)

(NOT A OR NOT B) AND (NOT A OR NOT C) AND (NOT B OR NOT C)

3 cláusulas, 6 literales · complete · Ley de De Morgan ×1

Forma normal disyuntiva (FND)

(NOT A AND NOT B) OR (NOT A AND NOT C) OR (NOT B AND NOT C)

3 términos, 6 literales · complete · Ley de De Morgan ×1, Distributividad ×2, Absorción ×2, Idempotencia ×1

Solo NAND, solo NOR

Forma solo con NAND

NAND(NAND(NAND(NAND(NAND(A, B), NAND(A, C)), NAND(NAND(A, B), NAND(A, C))), NAND(B, C)), NAND(NAND(NAND(NAND(A, B), NAND(A, C)), NAND(NAND(A, B), NAND(A, C))), NAND(B, C)))

19 compuertas · equivalencia verificada en todas las filas

Forma solo con NOR

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

19 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: 4ef3ccb1fa7e9a22f2faf9222cfc7bf8399a90e1255462fca845fba40d234c5a
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

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

Outputs (declared order) and their functions:
    'at_most_one' -> output_at_most_one(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, 'C': False})
    # -> {'at_most_one': True}
    model.output_at_most_one({'A': False, 'B': False, 'C': False})
    # -> True
    model.evaluate({'A': 1, 'B': False, 'C': 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_at_most_one",
]

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": "4ef3ccb1fa7e9a22f2faf9222cfc7bf8399a90e1255462fca845fba40d234c5a",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "at_most_one",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_f70ccd60a05f": {"kind": "input", "name": "A", "identifier": "input_A"},
    "v_b3b35c3e81d9": {"kind": "input", "name": "B", "identifier": "input_B"},
    "v_5c5313465744": {"kind": "input", "name": "C", "identifier": "input_C"},
    "f_b08deefb996d": {"kind": "output", "name": "at_most_one", "identifier": "output_at_most_one"},
}

INPUT_NAMES = ("A", "B", "C")
OUTPUT_NAMES = ("at_most_one",)
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_at_most_one(input_A: bool, input_B: bool, input_C: bool) -> bool:
    """Decision output 'at_most_one': NOT ((A AND B) OR (A AND C) OR (B AND C))"""
    return not ((input_A and input_B) or (input_A and input_C) or (input_B and input_C))


# ---- Public API ----


def output_at_most_one(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'at_most_one' (stable ID f_b08deefb996d).

    'at_most_one' := NOT ((A AND B) OR (A AND C) OR (B AND C))

    Required inputs: 'A', 'B', 'C'.
    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", "C"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_at_most_one_value = _compute_output_at_most_one(values["A"], values["B"], values["C"])
    return output_at_most_one_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', 'C'.
    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", "C"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_at_most_one_value = _compute_output_at_most_one(values["A"], values["B"], values["C"])
    return {
        "at_most_one": output_at_most_one_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: 4ef3ccb1fa7e9a22f2faf9222cfc7bf8399a90e1255462fca845fba40d234c5a
-- 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_f70ccd60a05f: A -> "in_A"
--   v_b3b35c3e81d9: B -> "in_B"
--   v_5c5313465744: C -> "in_C"
-- Output columns:
--   f_b08deefb996d: at_most_one -> "out_at_most_one"
-- 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_at_most_one] output at_most_one (stable ID f_b08deefb996d)
--   at_most_one := NOT ((A AND B) OR (A AND C) OR (B AND C))
--   PRECONDITION: "in_A", "in_B", "in_C" are non-NULL booleans.
--   (NOT (("in_A" AND "in_B") OR ("in_A" AND "in_C") OR ("in_B" AND "in_C")))
--

-- ----------------------------------------------------------------------------------------------
-- 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", "in_C") AS (
  -- tangibool:example-rows:begin
  VALUES
    (1, FALSE, FALSE, FALSE),
    (2, FALSE, FALSE, TRUE),
    (3, FALSE, TRUE, FALSE),
    (4, FALSE, TRUE, TRUE),
    (5, TRUE, FALSE, FALSE),
    (6, TRUE, FALSE, TRUE),
    (7, TRUE, TRUE, FALSE),
    (8, TRUE, TRUE, TRUE),
    (9, NULL::boolean, FALSE, FALSE)
  -- tangibool:example-rows:end
),
checked AS (
  SELECT
    inputs.*,
    CASE
      WHEN "in_A" IS NULL OR "in_B" IS NULL OR "in_C" IS NULL THEN 'invalid_input'
      ELSE 'ok'
    END AS "status"
  FROM inputs
)
SELECT
  "row_id",
  "in_A",
  "in_B",
  "in_C",
  "status",
  CASE WHEN "status" = 'ok' THEN (NOT (("in_A" AND "in_B") OR ("in_A" AND "in_C") OR ("in_B" AND "in_C"))) END AS "out_at_most_one"
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: 4ef3ccb1fa7e9a22f2faf9222cfc7bf8399a90e1255462fca845fba40d234c5a
; 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_f70ccd60a05f: A -> in_A
;   v_b3b35c3e81d9: B -> in_B
;   v_5c5313465744: C -> in_C
;   f_b08deefb996d: at_most_one -> out_at_most_one

(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
(declare-const in_C Bool) ; C

; ---- Outputs (dependencies first)
; output at_most_one (stable ID f_b08deefb996d): NOT ((A AND B) OR (A AND C) OR (B AND C))
(define-fun out_at_most_one () Bool (not (or (and in_A in_B) (and in_A in_C) (and in_B in_C))))

; ---- 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
at_most_one = \overline{\left(A \cdot B\right) + \left(A \cdot C\right) + \left(B \cdot C\right)}

El error habitual

Confundir «como máximo uno» con «exactamente uno». Coinciden en siete de las ocho filas y difieren en la fila de todo falso, A = FALSO, B = FALSO, C = FALSO, donde «como máximo uno» es verdadero y «exactamente uno» es falso. La fila 0 de la tabla es la forma más rápida de distinguirlas.

Preguntas

¿Por qué las construcciones NAND y NOR necesitan aquí el mismo número de compuertas?

Los dos conversores informan de 19 compuertas para esta expresión, una negación que envuelve una mayoría. Los recuentos describen la construcción mecánica de cada familia, vuelta a evaluar contra la tabla, no la función en sí; aquí las dos construcciones resultan del mismo tamaño, cosa que no ocurre en la mayoría de las páginas.

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.