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Condiciones encadenadas: si premium entonces (descuento → aprobado)

Una condición anidada, premium -> (discount -> approved): si el cliente es premium, entonces cualquier descuento debe estar aprobado. Ocho filas con una sola que falla, una FNC de una cláusula y un mapa de Karnaugh con tres grupos de cuatro celdas.

La regla es una implicación cuyo consecuente es a su vez una implicación. Leída como política: para los clientes premium, un descuento requiere aprobación; para los demás la regla no dice nada. La lectura del motor la desarma como «si premium es verdadero, entonces: si discount es verdadero, entonces approved es verdadero». En la tabla de ocho filas la salida es verdadera en siete filas y falsa exactamente en una, ΠM(6): premium = VERDADERO, discount = VERDADERO, approved = FALSO, un cliente premium con un descuento sin aprobar.

Las comprobaciones: no es tautología, contraejemplo premium = VERDADERO, discount = VERDADERO, approved = FALSO; satisfacible, testigo las tres entradas en FALSO. El simplificador no encontró ninguna ley que acorte la expresión de dos operadores, que imprime como premium -> discount -> approved. El conversor aplica dos eliminaciones de la implicación y las dos formas salen como NOT premium OR NOT discount OR approved: como FNC una sola cláusula de tres literales, como FND tres términos de un literal. El mapa dibuja NOT premium, NOT discount y approved, tres grupos de cuatro celdas, tres implicantes primos, y deriva la misma cláusula. Compuertas: 5 NAND, 17 NOR.

Las implicaciones anidadas son como se escriben las políticas con alcance: dentro del segmento premium, se aplica una regla adicional. La forma de cláusula muestra lo que la regla prohíbe de verdad, la única combinación de premium y descuento sin aprobación, que es el único caso de prueba negativo. Lo que hay que vigilar es cuánto no dice la regla: seis de las siete filas verdaderas son vacuas, incluidas todas las filas en que premium es falso, así que una batería de pruebas sin clientes premium no ejercita nada. La página del error de la verdad vacua trata esta trampa en detalle.

rule := premium -> (discount -> approved) · salida

Comprobaciones exactas

¿Siempre verdadera (tautología)? No Contraejemplo: premium = V, discount = V, approved = F · Exhaustively checked and solver cross-checked · 7 filas
¿Siempre falsa (contradicción)? No Contraejemplo: premium = F, discount = F, approved = F · Exhaustively checked and solver cross-checked · 1 filas
¿Puede ser verdadera? Sí Asignación de ejemplo: premium = F, discount = F, approved = F · Exhaustively checked and solver cross-checked · 1 filas

Tabla de verdad (8 filas)

Condiciones encadenadas: si premium entonces (descuento → aprobado)
#premiumdiscountapprovedrule
0FALSOFALSOFALSOVERDADERO
1FALSOFALSOVERDADEROVERDADERO
2FALSOVERDADEROFALSOVERDADERO
3FALSOVERDADEROVERDADEROVERDADERO
4VERDADEROFALSOFALSOVERDADERO
5VERDADEROFALSOVERDADEROVERDADERO
6VERDADEROVERDADEROFALSOFALSO
7VERDADEROVERDADEROVERDADEROVERDADERO

Lectura en lenguaje claro

Principianterule es verdadero exactamente cuando se cumple la siguiente condición «si… entonces»:
1. si: premium es verdadero
2. entonces: si discount es verdadero, entonces approved es verdadero
En todos los demás casos, rule 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.
Intermediorule es verdadero exactamente cuando se cumple la siguiente condición «si… entonces»:
1. si: premium es verdadero
2. entonces: si discount es verdadero, entonces approved es verdadero
En todos los demás casos, rule es falso.
Avanzadorule es verdadero exactamente cuando se cumple la siguiente condición «si… entonces» [implicación material →]:
1. si: premium es verdadero
2. entonces: si discount es verdadero, entonces approved es verdadero [implicación material →]
En todos los demás casos, rule es falso.
Notación formal: premium → (discount → approved)

Diagrama lógico

Chained conditions: if premium then (discount → approved) Chained conditions: if premium then (discount → approved) premium discount approved rule → →
Chained conditions: if premium then (discount → approved) Chained conditions: if premium then (discount → approved) premium discount approved rule → →

Simplificación paso a paso

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

premium -> discount -> approved

Mapa de Karnaugh

premium \ discountapproved
00
01
11
10
0
1m0
1m1
1m3
1m2
1
1m4
1m5
1m7
0m6
  1. celdas m0, m1, m2, m3NOT premium
  2. celdas m0, m1, m4, m5NOT discount
  3. celdas m1, m3, m5, m7approved
NOT premium OR NOT discount OR approved · Derived NOT premium OR NOT discount OR approved. Exactly equivalent on every assignment.

Mintérminos y maxtérminos

rule(premium, discount, approved) = Σm(0, 1, 2, 3, 4, 5, 7)rule(premium, discount, approved) = ΠM(6)

Orden de variables: premium, discount, approved · 8 filas

Formas normales

Forma normal conjuntiva (FNC)

NOT premium OR NOT discount OR approved

1 cláusulas, 3 literales · complete · Eliminación de la implicación ×2

Forma normal disyuntiva (FND)

NOT premium OR NOT discount OR approved

3 términos, 3 literales · complete · Eliminación de la implicación ×2

Solo NAND, solo NOR

Forma solo con NAND

NAND(NAND(NAND(premium, discount), NAND(premium, discount)), NAND(approved, approved))

5 compuertas · equivalencia verificada en todas las filas

Forma solo con NOR

NOR(NOR(NOR(NOR(NOR(premium, premium), NOR(discount, discount)), NOR(NOR(premium, premium), NOR(discount, discount))), approved), NOR(NOR(NOR(NOR(premium, premium), NOR(discount, discount)), NOR(NOR(premium, premium), NOR(discount, discount))), approved))

17 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: 3812802ee09cc9f1e28b9881873ee61e370dfb9d395130fdebe8d159bde58afe
Assumptions digest: d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78

Inputs (dict keys, declared order) and generated parameter names:
    'premium' -> input_premium
    'discount' -> input_discount
    'approved' -> input_approved

Outputs (declared order) and their functions:
    'rule' -> output_rule(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({'premium': False, 'discount': False, 'approved': False})
    # -> {'rule': True}
    model.output_rule({'premium': False, 'discount': False, 'approved': False})
    # -> True
    model.evaluate({'premium': 1, 'discount': False, 'approved': 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_rule",
]

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": "3812802ee09cc9f1e28b9881873ee61e370dfb9d395130fdebe8d159bde58afe",
    "assumptions_digest": "d58ee1ad38e23e5093e4baa1047b5ae70d6972e08f555e34ac3fa16df63c4a78",
    "primary_output": "rule",
    "unused_inputs": (),
    "subcircuits": (),
}

# Stable ID -> generated identifier (reversible mapping; names are exact, escaped strings).
IDENTIFIER_MAP = {
    "v_c5c42e32ffc1": {"kind": "input", "name": "premium", "identifier": "input_premium"},
    "v_509f7cfe4a0b": {"kind": "input", "name": "discount", "identifier": "input_discount"},
    "v_5bfb01fad5b4": {"kind": "input", "name": "approved", "identifier": "input_approved"},
    "f_0d32929ab552": {"kind": "output", "name": "rule", "identifier": "output_rule"},
}

INPUT_NAMES = ("premium", "discount", "approved")
OUTPUT_NAMES = ("rule",)
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_rule(input_premium: bool, input_discount: bool, input_approved: bool) -> bool:
    """Decision output 'rule': premium -> (discount -> approved)"""
    return (not input_premium) or ((not input_discount) or input_approved)


# ---- Public API ----


def output_rule(inputs: Mapping[str, bool], *, check_scope: bool = True) -> bool:
    """Decision output 'rule' (stable ID f_0d32929ab552).

    'rule' := premium -> (discount -> approved)

    Required inputs: 'premium', 'discount', 'approved'.
    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, ("premium", "discount", "approved"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_rule_value = _compute_output_rule(values["premium"], values["discount"], values["approved"])
    return output_rule_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: 'premium', 'discount', 'approved'.
    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, ("premium", "discount", "approved"), check_scope)
    if check_scope:
        _require_in_scope(values)
    output_rule_value = _compute_output_rule(values["premium"], values["discount"], values["approved"])
    return {
        "rule": output_rule_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: 3812802ee09cc9f1e28b9881873ee61e370dfb9d395130fdebe8d159bde58afe
-- 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_c5c42e32ffc1: premium -> "in_premium"
--   v_509f7cfe4a0b: discount -> "in_discount"
--   v_5bfb01fad5b4: approved -> "in_approved"
-- Output columns:
--   f_0d32929ab552: rule -> "out_rule"
-- 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_rule] output rule (stable ID f_0d32929ab552)
--   rule := premium -> (discount -> approved)
--   PRECONDITION: "in_premium", "in_discount", "in_approved" are non-NULL booleans.
--   ((NOT "in_premium") OR ((NOT "in_discount") OR "in_approved"))
--

-- ----------------------------------------------------------------------------------------------
-- 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_premium", "in_discount", "in_approved") 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_premium" IS NULL OR "in_discount" IS NULL OR "in_approved" IS NULL THEN 'invalid_input'
      ELSE 'ok'
    END AS "status"
  FROM inputs
)
SELECT
  "row_id",
  "in_premium",
  "in_discount",
  "in_approved",
  "status",
  CASE WHEN "status" = 'ok' THEN ((NOT "in_premium") OR ((NOT "in_discount") OR "in_approved")) END AS "out_rule"
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: 3812802ee09cc9f1e28b9881873ee61e370dfb9d395130fdebe8d159bde58afe
; 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_c5c42e32ffc1: premium -> in_premium
;   v_509f7cfe4a0b: discount -> in_discount
;   v_5bfb01fad5b4: approved -> in_approved
;   f_0d32929ab552: rule -> out_rule

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

; ---- Inputs (declared order)
(declare-const in_premium Bool) ; premium
(declare-const in_discount Bool) ; discount
(declare-const in_approved Bool) ; approved

; ---- Outputs (dependencies first)
; output rule (stable ID f_0d32929ab552): premium -> (discount -> approved)
(define-fun out_rule () Bool (=> in_premium (=> in_discount in_approved)))

; ---- 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
rule = \mathit{premium} \rightarrow \left(\mathit{discount} \rightarrow \mathit{approved}\right)

El error habitual

Leer la regla como si exigiera aprobación para todo descuento. Para los clientes no premium, premium = FALSO, la regla es verdadera en las cuatro filas sean cuales sean discount y approved. La cláusula NOT premium OR NOT discount OR approved hace explícito el alcance: el primer literal desconecta toda la regla.

Preguntas

¿premium -> (discount -> approved) es lo mismo que (premium AND discount) -> approved?

La FNC de esta página, NOT premium OR NOT discount OR approved, es a lo que también se reduce la segunda forma por eliminación de la implicación y De Morgan, y el comprobador de equivalencia confirmará que las dos coinciden en las ocho filas. Las dos dicen: la aprobación solo hace falta cuando premium y descuento se dan juntos.

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.