AND solo con compuertas NOR
A AND B construido con tres compuertas NOR, NOR(NOR(A, A), NOR(B, B)), y comprobado contra el AND llano. La equivalencia se cumple en las cuatro filas, y el conversor NOR del motor produce exactamente el mismo circuito.
Un NOR con las dos entradas unidas es un inversor, y un NOR de dos señales invertidas es, por De Morgan, su AND: no (no A o no B) es A y B. El modelo lleva A AND B como referencia y la fórmula de tres NOR como circuito. La lectura del motor dice que conjunction es verdadero exactamente cuando A y B son ambos verdaderos; la tabla muestra las dos columnas verdaderas solo en la fila 3, Σm(3).
La equivalencia se cumple: conjunction y with_nors coinciden en todas las filas. El AND no es tautología, contraejemplo A = FALSO, B = FALSO, y es satisfacible con el testigo A = VERDADERO, B = VERDADERO. El simplificador no encontró ninguna ley aplicable, y los dos conversores a formas normales devuelven A AND B sin cambios. El mapa de Karnaugh dibuja un grupo en la celda 3, un implicante primo. El conversor solo con NOR produce NOR(NOR(A, A), NOR(B, B)), 3 compuertas, carácter por carácter el circuito de esta página; el conversor solo con NAND produce NAND(NAND(A, B), NAND(A, B)), también 3.
Es el caso base de la universalidad del NOR: con NOT y AND disponibles, y OR a partir de un solo NOR más un inversor, se puede construir cualquier función. Los recuentos importan en la práctica porque una familia NOR construye el OR barato y el AND caro, mientras que NAND hace lo contrario, y la página de comparativa suma los dos para expresiones mayores. Lo que hay que vigilar es el coste del inversor: cada NOR con entradas unidas es una compuerta completa, así que tres compuertas por un AND es el precio de una biblioteca con un solo tipo de compuerta.
conjunction := A AND B · salidawith_nors := NOR(NOR(A, A), NOR(B, B)) · salida
Comprobaciones exactas
Tabla de verdad (4 filas)
| # | A | B | conjunction | with_nors |
|---|---|---|---|---|
| 0 | FALSO | FALSO | FALSO | FALSO |
| 1 | FALSO | VERDADERO | FALSO | FALSO |
| 2 | VERDADERO | FALSO | FALSO | FALSO |
| 3 | VERDADERO | VERDADERO | VERDADERO | VERDADERO |
Lectura en lenguaje claro
Principianteconjunction es verdadero exactamente cuando A y B son ambos verdaderos.
En todos los demás casos, conjunction es falso.
Sobre los operadores:
- AND (Y) solo es verdadero cuando todas sus partes son verdaderas.
Intermedioconjunction es verdadero exactamente cuando A y B son ambos verdaderos.
En todos los demás casos, conjunction es falso.
Avanzadoconjunction es verdadero exactamente cuando A y B son ambos verdaderos [conjunción ∧].
En todos los demás casos, conjunction es falso.
Notación formal: A ∧ B
Diagrama lógico
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 AND B
Mapa de Karnaugh
- celdas m3
A AND B
Mintérminos y maxtérminos
conjunction(A, B) = Σm(3)conjunction(A, B) = ΠM(0, 1, 2)Orden de variables: A, B · 4 filas
Formas normales
Forma normal conjuntiva (FNC)
A AND B2 cláusulas, 2 literales · complete
Forma normal disyuntiva (FND)
A AND B1 términos, 2 literales · complete
Solo NAND, solo NOR
Forma solo con NAND
NAND(NAND(A, B), NAND(A, B))3 compuertas · equivalencia verificada en todas las filas
Forma solo con NOR
NOR(NOR(A, A), NOR(B, B))3 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: 56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78
Inputs (dict keys, declared order) and generated parameter names:
'A' -> input_A
'B' -> input_B
Outputs (declared order) and their functions:
'conjunction' -> output_conjunction(inputs)
'with_nors' -> output_with_nors(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})
# -> {'conjunction': False, 'with_nors': False}
model.output_conjunction({'A': False, 'B': False})
# -> False
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_conjunction",
"output_with_nors",
]
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": "56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b",
"assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
"primary_output": "conjunction",
"unused_inputs": (),
"subcircuits": (),
}
# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
"v_51ec16294c5a": {"kind": "input", "name": "A", "identifier": "input_A"},
"v_15897b676c41": {"kind": "input", "name": "B", "identifier": "input_B"},
"f_dd96c92ea782": {"kind": "output", "name": "conjunction", "identifier": "output_conjunction"},
"f_d4997667f875": {"kind": "output", "name": "with_nors", "identifier": "output_with_nors"},
}
INPUT_NAMES = ("A", "B")
OUTPUT_NAMES = ("conjunction", "with_nors")
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_conjunction(input_A: bool, input_B: bool) -> bool:
"""Decision output 'conjunction': A AND B"""
return input_A and input_B
def _compute_output_with_nors(input_A: bool, input_B: bool) -> bool:
"""Decision output 'with_nors': NOR(NOR(A, A), NOR(B, B))"""
return not ((not (input_A or input_A)) or (not (input_B or input_B)))
# ---- Public API ----
def output_conjunction(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'conjunction' (stable ID f_dd96c92ea782).
'conjunction' := A AND 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_conjunction_value = _compute_output_conjunction(values["A"], values["B"])
return output_conjunction_value
def output_with_nors(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
"""Decision output 'with_nors' (stable ID f_d4997667f875).
'with_nors' := NOR(NOR(A, A), NOR(B, 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_with_nors_value = _compute_output_with_nors(values["A"], values["B"])
return output_with_nors_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_conjunction_value = _compute_output_conjunction(values["A"], values["B"])
output_with_nors_value = _compute_output_with_nors(values["A"], values["B"])
return {
"conjunction": output_conjunction_value,
"with_nors": output_with_nors_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: 56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b
-- 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_51ec16294c5a: A -> "in_A"
-- v_15897b676c41: B -> "in_B"
-- Output columns:
-- f_dd96c92ea782: conjunction -> "out_conjunction"
-- f_d4997667f875: with_nors -> "out_with_nors"
-- 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_conjunction] output conjunction (stable ID f_dd96c92ea782)
-- conjunction := A AND B
-- PRECONDITION: "in_A", "in_B" are non-NULL booleans.
-- ("in_A" AND "in_B")
--
-- [out_with_nors] output with_nors (stable ID f_d4997667f875)
-- with_nors := NOR(NOR(A, A), NOR(B, B))
-- PRECONDITION: "in_A", "in_B" are non-NULL booleans.
-- (NOT ((NOT ("in_A" OR "in_A")) OR (NOT ("in_B" 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 ("in_A" AND "in_B") END AS "out_conjunction",
CASE WHEN "status" = 'ok' THEN (NOT ((NOT ("in_A" OR "in_A")) OR (NOT ("in_B" OR "in_B")))) END AS "out_with_nors"
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: 56baf426f3aa5d4113677dc2aa69982817b402aee01008c9cbfca5b31c3d7f1b ; 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_51ec16294c5a: A -> in_A ; v_15897b676c41: B -> in_B ; f_dd96c92ea782: conjunction -> out_conjunction ; f_d4997667f875: with_nors -> out_with_nors (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 conjunction (stable ID f_dd96c92ea782): A AND B (define-fun out_conjunction () Bool (and in_A in_B)) ; output with_nors (stable ID f_d4997667f875): NOR(NOR(A, A), NOR(B, B)) (define-fun out_with_nors () Bool (not (or (not (or in_A in_A)) (not (or in_B 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
conjunction = A \cdot B
with_nors = \overline{\overline{A + A} + \overline{B + B}}El error habitual
Usar NOR(A, B) como AND porque es falso en tres filas y verdadero en una. Es verdadero en la fila equivocada, A = FALSO, B = FALSO; el AND lo es en A = VERDADERO, B = VERDADERO. Los inversores mueven la única fila verdadera de la 0 a la 3, como muestra la tabla.
Preguntas
¿Por qué la forma NAND del AND también tiene 3 compuertas?
NAND(A, B) ya es NOT AND, así que un NAND más cableado como inversor restaura el AND, y el conversor cuenta las dos compuertas de la etapa inversora con la primera: NAND(NAND(A, B), NAND(A, B)). Las dos familias necesitan 3 para esta función, por razones distintas.
Relacionado
Operadores y compuertas
- Compuerta AND (puerta Y): tabla de verdad de 2, 3 y 4 entradas, símbolo y expresión
- Compuerta NOR: tabla de verdad de 2, 3 y 4 entradas, compuerta universal
Herramientas
- Generador de tablas de verdad: cualquier expresión booleana, hasta 12 variables, con columnas intermedias
- Calculadora y simplificador de álgebra de Boole paso a paso (cada ley nombrada y verificada)
- Solucionador de mapas de Karnaugh: de 2 a 4 variables, agrupaciones, SOP y POS
Ejercicios
Véase también
- AND con compuertas NOR: A·B = NOR(NOR(A, A), NOR(B, B))
- NOT con NOR: A' = NOR(A, A)
- OR con compuertas NOR: A + B = NOR(NOR(A, B), NOR(A, B))
- Semisumador, acarreo: A AND B
- NAND frente a NOR como compuerta universal
- Conversor a solo NAND o solo NOR con demostración de equivalencia
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.