TSTP Solution File: PUZ019-1 by Beagle---0.9.51
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- Process Solution
%------------------------------------------------------------------------------
% File : Beagle---0.9.51
% Problem : PUZ019-1 : TPTP v8.1.2. Bugfixed v5.1.0.
% Transfm : none
% Format : tptp:raw
% Command : java -Dfile.encoding=UTF-8 -Xms512M -Xmx4G -Xss10M -jar /export/starexec/sandbox2/solver/bin/beagle.jar -auto -q -proof -print tff -smtsolver /export/starexec/sandbox2/solver/bin/cvc4-1.4-x86_64-linux-opt -liasolver cooper -t %d %s
% Computer : n023.cluster.edu
% Model : x86_64 x86_64
% CPU : Intel(R) Xeon(R) CPU E5-2620 v4 2.10GHz
% Memory : 8042.1875MB
% OS : Linux 3.10.0-693.el7.x86_64
% CPULimit : 300s
% WCLimit : 300s
% DateTime : Tue Aug 22 10:54:01 EDT 2023
% Result : Unsatisfiable 4.63s 2.08s
% Output : CNFRefutation 4.94s
% Verified :
% SZS Type : Refutation
% Derivation depth : 11
% Number of leaves : 35
% Syntax : Number of formulae : 151 ( 82 unt; 19 typ; 0 def)
% Number of atoms : 241 ( 0 equ)
% Maximal formula atoms : 8 ( 1 avg)
% Number of connectives : 224 ( 115 ~; 109 |; 0 &)
% ( 0 <=>; 0 =>; 0 <=; 0 <~>)
% Maximal formula depth : 17 ( 3 avg)
% Maximal term depth : 1 ( 1 avg)
% Number of types : 2 ( 0 usr)
% Number of type conns : 11 ( 7 >; 4 *; 0 +; 0 <<)
% Number of predicates : 8 ( 7 usr; 1 prp; 0-2 aty)
% Number of functors : 12 ( 12 usr; 12 con; 0-0 aty)
% Number of variables : 96 (; 96 !; 0 ?; 0 :)
% Comments :
%------------------------------------------------------------------------------
%$ husband > has_job > equal_people > equal_jobs > male > female > educated > #nlpp > thelma > teacher > steve > roberta > police > pete > operator > nurse > guard > chef > boxer > actor
%Foreground sorts:
%Background operators:
%Foreground operators:
tff(roberta,type,
roberta: $i ).
tff(nurse,type,
nurse: $i ).
tff(husband,type,
husband: ( $i * $i ) > $o ).
tff(pete,type,
pete: $i ).
tff(has_job,type,
has_job: ( $i * $i ) > $o ).
tff(boxer,type,
boxer: $i ).
tff(teacher,type,
teacher: $i ).
tff(guard,type,
guard: $i ).
tff(steve,type,
steve: $i ).
tff(female,type,
female: $i > $o ).
tff(operator,type,
operator: $i ).
tff(equal_jobs,type,
equal_jobs: ( $i * $i ) > $o ).
tff(police,type,
police: $i ).
tff(actor,type,
actor: $i ).
tff(equal_people,type,
equal_people: ( $i * $i ) > $o ).
tff(educated,type,
educated: $i > $o ).
tff(thelma,type,
thelma: $i ).
tff(chef,type,
chef: $i ).
tff(male,type,
male: $i > $o ).
tff(f_269,axiom,
! [X5,X6,X3,X7,X1,X2,X8,X4] :
( ~ has_job(X1,chef)
| ~ has_job(X2,guard)
| ~ has_job(X3,nurse)
| ~ has_job(X4,operator)
| ~ has_job(X5,police)
| ~ has_job(X6,teacher)
| ~ has_job(X7,actor)
| ~ has_job(X8,boxer) ),
file(unknown,unknown) ).
tff(f_242,axiom,
male(steve),
file(unknown,unknown) ).
tff(f_156,axiom,
! [X] :
( ~ male(X)
| ~ female(X) ),
file(unknown,unknown) ).
tff(f_243,axiom,
male(pete),
file(unknown,unknown) ).
tff(f_237,axiom,
~ has_job(roberta,chef),
file(unknown,unknown) ).
tff(f_217,axiom,
! [X] :
( has_job(roberta,X)
| has_job(thelma,X)
| has_job(pete,X)
| has_job(steve,X) ),
file(unknown,unknown) ).
tff(f_235,axiom,
~ educated(pete),
file(unknown,unknown) ).
tff(f_241,axiom,
~ has_job(roberta,police),
file(unknown,unknown) ).
tff(f_144,axiom,
! [X] :
( ~ has_job(X,police)
| educated(X) ),
file(unknown,unknown) ).
tff(f_150,axiom,
! [X] :
( ~ has_job(X,chef)
| ~ has_job(X,police) ),
file(unknown,unknown) ).
tff(f_129,axiom,
! [X] :
( ~ has_job(X,chef)
| female(X) ),
file(unknown,unknown) ).
tff(f_245,axiom,
female(thelma),
file(unknown,unknown) ).
tff(f_244,axiom,
female(roberta),
file(unknown,unknown) ).
tff(f_119,axiom,
! [X] :
( ~ has_job(X,nurse)
| male(X) ),
file(unknown,unknown) ).
tff(f_134,axiom,
! [X] :
( ~ has_job(X,nurse)
| educated(X) ),
file(unknown,unknown) ).
tff(f_124,axiom,
! [X] :
( ~ has_job(X,actor)
| male(X) ),
file(unknown,unknown) ).
tff(c_128,plain,
! [X7_36,X8_39,X5_33,X6_34,X1_37,X4_40,X2_38,X3_35] :
( ~ has_job(X8_39,boxer)
| ~ has_job(X7_36,actor)
| ~ has_job(X6_34,teacher)
| ~ has_job(X5_33,police)
| ~ has_job(X4_40,operator)
| ~ has_job(X3_35,nurse)
| ~ has_job(X2_38,guard)
| ~ has_job(X1_37,chef) ),
inference(cnfTransformation,[status(thm)],[f_269]) ).
tff(c_436,plain,
! [X1_37] : ~ has_job(X1_37,chef),
inference(splitLeft,[status(thm)],[c_128]) ).
tff(c_120,plain,
male(steve),
inference(cnfTransformation,[status(thm)],[f_242]) ).
tff(c_132,plain,
! [X_44] :
( ~ female(X_44)
| ~ male(X_44) ),
inference(cnfTransformation,[status(thm)],[f_156]) ).
tff(c_143,plain,
~ female(steve),
inference(resolution,[status(thm)],[c_120,c_132]) ).
tff(c_122,plain,
male(pete),
inference(cnfTransformation,[status(thm)],[f_243]) ).
tff(c_144,plain,
~ female(pete),
inference(resolution,[status(thm)],[c_122,c_132]) ).
tff(c_114,plain,
~ has_job(roberta,chef),
inference(cnfTransformation,[status(thm)],[f_237]) ).
tff(c_108,plain,
! [X_31] :
( has_job(steve,X_31)
| has_job(pete,X_31)
| has_job(thelma,X_31)
| has_job(roberta,X_31) ),
inference(cnfTransformation,[status(thm)],[f_217]) ).
tff(c_112,plain,
~ educated(pete),
inference(cnfTransformation,[status(thm)],[f_235]) ).
tff(c_118,plain,
~ has_job(roberta,police),
inference(cnfTransformation,[status(thm)],[f_241]) ).
tff(c_214,plain,
! [X_70] :
( has_job(steve,X_70)
| has_job(pete,X_70)
| has_job(thelma,X_70)
| has_job(roberta,X_70) ),
inference(cnfTransformation,[status(thm)],[f_217]) ).
tff(c_88,plain,
! [X_12] :
( educated(X_12)
| ~ has_job(X_12,police) ),
inference(cnfTransformation,[status(thm)],[f_144]) ).
tff(c_236,plain,
( educated(thelma)
| has_job(steve,police)
| has_job(pete,police)
| has_job(roberta,police) ),
inference(resolution,[status(thm)],[c_214,c_88]) ).
tff(c_260,plain,
( educated(thelma)
| has_job(steve,police)
| has_job(pete,police) ),
inference(negUnitSimplification,[status(thm)],[c_118,c_236]) ).
tff(c_264,plain,
has_job(pete,police),
inference(splitLeft,[status(thm)],[c_260]) ).
tff(c_278,plain,
educated(pete),
inference(resolution,[status(thm)],[c_264,c_88]) ).
tff(c_285,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_112,c_278]) ).
tff(c_287,plain,
~ has_job(pete,police),
inference(splitRight,[status(thm)],[c_260]) ).
tff(c_90,plain,
! [X_13] :
( ~ has_job(X_13,police)
| ~ has_job(X_13,chef) ),
inference(cnfTransformation,[status(thm)],[f_150]) ).
tff(c_224,plain,
( ~ has_job(thelma,chef)
| has_job(steve,police)
| has_job(pete,police)
| has_job(roberta,police) ),
inference(resolution,[status(thm)],[c_214,c_90]) ).
tff(c_253,plain,
( ~ has_job(thelma,chef)
| has_job(steve,police)
| has_job(pete,police) ),
inference(negUnitSimplification,[status(thm)],[c_118,c_224]) ).
tff(c_289,plain,
( ~ has_job(thelma,chef)
| has_job(steve,police) ),
inference(negUnitSimplification,[status(thm)],[c_287,c_253]) ).
tff(c_290,plain,
~ has_job(thelma,chef),
inference(splitLeft,[status(thm)],[c_289]) ).
tff(c_297,plain,
( has_job(steve,chef)
| has_job(pete,chef)
| has_job(roberta,chef) ),
inference(resolution,[status(thm)],[c_108,c_290]) ).
tff(c_300,plain,
( has_job(steve,chef)
| has_job(pete,chef) ),
inference(negUnitSimplification,[status(thm)],[c_114,c_297]) ).
tff(c_301,plain,
has_job(pete,chef),
inference(splitLeft,[status(thm)],[c_300]) ).
tff(c_82,plain,
! [X_9] :
( female(X_9)
| ~ has_job(X_9,chef) ),
inference(cnfTransformation,[status(thm)],[f_129]) ).
tff(c_308,plain,
female(pete),
inference(resolution,[status(thm)],[c_301,c_82]) ).
tff(c_314,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_144,c_308]) ).
tff(c_315,plain,
has_job(steve,chef),
inference(splitRight,[status(thm)],[c_300]) ).
tff(c_334,plain,
female(steve),
inference(resolution,[status(thm)],[c_315,c_82]) ).
tff(c_340,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_143,c_334]) ).
tff(c_342,plain,
has_job(thelma,chef),
inference(splitRight,[status(thm)],[c_289]) ).
tff(c_439,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_436,c_342]) ).
tff(c_440,plain,
! [X7_36,X8_39,X5_33,X6_34,X4_40,X2_38,X3_35] :
( ~ has_job(X3_35,nurse)
| ~ has_job(X5_33,police)
| ~ has_job(X7_36,actor)
| ~ has_job(X8_39,boxer)
| ~ has_job(X6_34,teacher)
| ~ has_job(X4_40,operator)
| ~ has_job(X2_38,guard) ),
inference(splitRight,[status(thm)],[c_128]) ).
tff(c_537,plain,
! [X2_38] : ~ has_job(X2_38,guard),
inference(splitLeft,[status(thm)],[c_440]) ).
tff(c_539,plain,
! [X2_89] : ~ has_job(X2_89,guard),
inference(splitLeft,[status(thm)],[c_440]) ).
tff(c_543,plain,
( has_job(steve,guard)
| has_job(pete,guard)
| has_job(roberta,guard) ),
inference(resolution,[status(thm)],[c_108,c_539]) ).
tff(c_547,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_537,c_537,c_537,c_543]) ).
tff(c_548,plain,
! [X7_36,X8_39,X5_33,X6_34,X4_40,X3_35] :
( ~ has_job(X6_34,teacher)
| ~ has_job(X7_36,actor)
| ~ has_job(X3_35,nurse)
| ~ has_job(X5_33,police)
| ~ has_job(X8_39,boxer)
| ~ has_job(X4_40,operator) ),
inference(splitRight,[status(thm)],[c_440]) ).
tff(c_748,plain,
! [X4_40] : ~ has_job(X4_40,operator),
inference(splitLeft,[status(thm)],[c_548]) ).
tff(c_751,plain,
! [X4_97] : ~ has_job(X4_97,operator),
inference(splitLeft,[status(thm)],[c_548]) ).
tff(c_755,plain,
( has_job(steve,operator)
| has_job(pete,operator)
| has_job(roberta,operator) ),
inference(resolution,[status(thm)],[c_108,c_751]) ).
tff(c_759,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_748,c_748,c_748,c_755]) ).
tff(c_760,plain,
! [X7_36,X8_39,X5_33,X6_34,X3_35] :
( ~ has_job(X5_33,police)
| ~ has_job(X7_36,actor)
| ~ has_job(X6_34,teacher)
| ~ has_job(X3_35,nurse)
| ~ has_job(X8_39,boxer) ),
inference(splitRight,[status(thm)],[c_548]) ).
tff(c_799,plain,
! [X8_39] : ~ has_job(X8_39,boxer),
inference(splitLeft,[status(thm)],[c_760]) ).
tff(c_801,plain,
! [X8_100] : ~ has_job(X8_100,boxer),
inference(splitLeft,[status(thm)],[c_760]) ).
tff(c_805,plain,
( has_job(steve,boxer)
| has_job(pete,boxer)
| has_job(roberta,boxer) ),
inference(resolution,[status(thm)],[c_108,c_801]) ).
tff(c_809,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_799,c_799,c_799,c_805]) ).
tff(c_810,plain,
! [X6_34,X5_33,X7_36,X3_35] :
( ~ has_job(X6_34,teacher)
| ~ has_job(X5_33,police)
| ~ has_job(X7_36,actor)
| ~ has_job(X3_35,nurse) ),
inference(splitRight,[status(thm)],[c_760]) ).
tff(c_811,plain,
! [X3_35] : ~ has_job(X3_35,nurse),
inference(splitLeft,[status(thm)],[c_810]) ).
tff(c_126,plain,
female(thelma),
inference(cnfTransformation,[status(thm)],[f_245]) ).
tff(c_124,plain,
female(roberta),
inference(cnfTransformation,[status(thm)],[f_244]) ).
tff(c_78,plain,
! [X_7] :
( male(X_7)
| ~ has_job(X_7,nurse) ),
inference(cnfTransformation,[status(thm)],[f_119]) ).
tff(c_263,plain,
( male(thelma)
| has_job(steve,nurse)
| has_job(pete,nurse)
| has_job(roberta,nurse) ),
inference(resolution,[status(thm)],[c_214,c_78]) ).
tff(c_411,plain,
has_job(roberta,nurse),
inference(splitLeft,[status(thm)],[c_263]) ).
tff(c_425,plain,
male(roberta),
inference(resolution,[status(thm)],[c_411,c_78]) ).
tff(c_92,plain,
! [X_14] :
( ~ female(X_14)
| ~ male(X_14) ),
inference(cnfTransformation,[status(thm)],[f_156]) ).
tff(c_429,plain,
~ female(roberta),
inference(resolution,[status(thm)],[c_425,c_92]) ).
tff(c_433,plain,
$false,
inference(demodulation,[status(thm),theory(equality)],[c_124,c_429]) ).
tff(c_434,plain,
( has_job(pete,nurse)
| has_job(steve,nurse)
| male(thelma) ),
inference(splitRight,[status(thm)],[c_263]) ).
tff(c_441,plain,
male(thelma),
inference(splitLeft,[status(thm)],[c_434]) ).
tff(c_444,plain,
~ female(thelma),
inference(resolution,[status(thm)],[c_441,c_92]) ).
tff(c_448,plain,
$false,
inference(demodulation,[status(thm),theory(equality)],[c_126,c_444]) ).
tff(c_449,plain,
( has_job(steve,nurse)
| has_job(pete,nurse) ),
inference(splitRight,[status(thm)],[c_434]) ).
tff(c_456,plain,
has_job(pete,nurse),
inference(splitLeft,[status(thm)],[c_449]) ).
tff(c_84,plain,
! [X_10] :
( educated(X_10)
| ~ has_job(X_10,nurse) ),
inference(cnfTransformation,[status(thm)],[f_134]) ).
tff(c_475,plain,
educated(pete),
inference(resolution,[status(thm)],[c_456,c_84]) ).
tff(c_484,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_112,c_475]) ).
tff(c_485,plain,
has_job(steve,nurse),
inference(splitRight,[status(thm)],[c_449]) ).
tff(c_814,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_811,c_485]) ).
tff(c_815,plain,
! [X5_33,X6_34,X7_36] :
( ~ has_job(X5_33,police)
| ~ has_job(X6_34,teacher)
| ~ has_job(X7_36,actor) ),
inference(splitRight,[status(thm)],[c_810]) ).
tff(c_816,plain,
! [X7_36] : ~ has_job(X7_36,actor),
inference(splitLeft,[status(thm)],[c_815]) ).
tff(c_596,plain,
! [X4_40] : ~ has_job(X4_40,operator),
inference(splitLeft,[status(thm)],[c_548]) ).
tff(c_599,plain,
! [X4_91] : ~ has_job(X4_91,operator),
inference(splitLeft,[status(thm)],[c_548]) ).
tff(c_603,plain,
( has_job(steve,operator)
| has_job(pete,operator)
| has_job(roberta,operator) ),
inference(resolution,[status(thm)],[c_108,c_599]) ).
tff(c_607,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_596,c_596,c_596,c_603]) ).
tff(c_608,plain,
! [X7_36,X8_39,X5_33,X6_34,X3_35] :
( ~ has_job(X5_33,police)
| ~ has_job(X7_36,actor)
| ~ has_job(X6_34,teacher)
| ~ has_job(X3_35,nurse)
| ~ has_job(X8_39,boxer) ),
inference(splitRight,[status(thm)],[c_548]) ).
tff(c_666,plain,
! [X8_39] : ~ has_job(X8_39,boxer),
inference(splitLeft,[status(thm)],[c_608]) ).
tff(c_668,plain,
! [X8_95] : ~ has_job(X8_95,boxer),
inference(splitLeft,[status(thm)],[c_608]) ).
tff(c_672,plain,
( has_job(steve,boxer)
| has_job(pete,boxer)
| has_job(roberta,boxer) ),
inference(resolution,[status(thm)],[c_108,c_668]) ).
tff(c_676,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_666,c_666,c_666,c_672]) ).
tff(c_677,plain,
! [X6_34,X5_33,X7_36,X3_35] :
( ~ has_job(X6_34,teacher)
| ~ has_job(X5_33,police)
| ~ has_job(X7_36,actor)
| ~ has_job(X3_35,nurse) ),
inference(splitRight,[status(thm)],[c_608]) ).
tff(c_678,plain,
! [X3_35] : ~ has_job(X3_35,nurse),
inference(splitLeft,[status(thm)],[c_677]) ).
tff(c_681,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_678,c_485]) ).
tff(c_682,plain,
! [X5_33,X6_34,X7_36] :
( ~ has_job(X5_33,police)
| ~ has_job(X6_34,teacher)
| ~ has_job(X7_36,actor) ),
inference(splitRight,[status(thm)],[c_677]) ).
tff(c_683,plain,
! [X7_36] : ~ has_job(X7_36,actor),
inference(splitLeft,[status(thm)],[c_682]) ).
tff(c_450,plain,
~ male(thelma),
inference(splitRight,[status(thm)],[c_434]) ).
tff(c_80,plain,
! [X_8] :
( male(X_8)
| ~ has_job(X_8,actor) ),
inference(cnfTransformation,[status(thm)],[f_124]) ).
tff(c_261,plain,
( male(thelma)
| has_job(steve,actor)
| has_job(pete,actor)
| has_job(roberta,actor) ),
inference(resolution,[status(thm)],[c_214,c_80]) ).
tff(c_516,plain,
( has_job(steve,actor)
| has_job(pete,actor)
| has_job(roberta,actor) ),
inference(negUnitSimplification,[status(thm)],[c_450,c_261]) ).
tff(c_517,plain,
has_job(roberta,actor),
inference(splitLeft,[status(thm)],[c_516]) ).
tff(c_527,plain,
male(roberta),
inference(resolution,[status(thm)],[c_517,c_80]) ).
tff(c_530,plain,
~ female(roberta),
inference(resolution,[status(thm)],[c_527,c_92]) ).
tff(c_534,plain,
$false,
inference(demodulation,[status(thm),theory(equality)],[c_124,c_530]) ).
tff(c_535,plain,
( has_job(pete,actor)
| has_job(steve,actor) ),
inference(splitRight,[status(thm)],[c_516]) ).
tff(c_549,plain,
has_job(steve,actor),
inference(splitLeft,[status(thm)],[c_535]) ).
tff(c_686,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_683,c_549]) ).
tff(c_687,plain,
! [X5_33,X6_34] :
( ~ has_job(X5_33,police)
| ~ has_job(X6_34,teacher) ),
inference(splitRight,[status(thm)],[c_682]) ).
tff(c_688,plain,
! [X6_34] : ~ has_job(X6_34,teacher),
inference(splitLeft,[status(thm)],[c_687]) ).
tff(c_690,plain,
! [X6_96] : ~ has_job(X6_96,teacher),
inference(splitLeft,[status(thm)],[c_687]) ).
tff(c_694,plain,
( has_job(steve,teacher)
| has_job(pete,teacher)
| has_job(roberta,teacher) ),
inference(resolution,[status(thm)],[c_108,c_690]) ).
tff(c_698,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_688,c_688,c_688,c_694]) ).
tff(c_699,plain,
! [X5_33] : ~ has_job(X5_33,police),
inference(splitRight,[status(thm)],[c_687]) ).
tff(c_341,plain,
has_job(steve,police),
inference(splitRight,[status(thm)],[c_289]) ).
tff(c_702,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_699,c_341]) ).
tff(c_703,plain,
has_job(pete,actor),
inference(splitRight,[status(thm)],[c_535]) ).
tff(c_819,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_816,c_703]) ).
tff(c_820,plain,
! [X5_33,X6_34] :
( ~ has_job(X5_33,police)
| ~ has_job(X6_34,teacher) ),
inference(splitRight,[status(thm)],[c_815]) ).
tff(c_854,plain,
! [X6_34] : ~ has_job(X6_34,teacher),
inference(splitLeft,[status(thm)],[c_820]) ).
tff(c_856,plain,
! [X6_103] : ~ has_job(X6_103,teacher),
inference(splitLeft,[status(thm)],[c_820]) ).
tff(c_860,plain,
( has_job(steve,teacher)
| has_job(pete,teacher)
| has_job(roberta,teacher) ),
inference(resolution,[status(thm)],[c_108,c_856]) ).
tff(c_864,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_854,c_854,c_854,c_860]) ).
tff(c_865,plain,
! [X5_33] : ~ has_job(X5_33,police),
inference(splitRight,[status(thm)],[c_820]) ).
tff(c_868,plain,
$false,
inference(negUnitSimplification,[status(thm)],[c_865,c_341]) ).
%------------------------------------------------------------------------------
%----ORIGINAL SYSTEM OUTPUT
% 0.07/0.12 % Problem : PUZ019-1 : TPTP v8.1.2. Bugfixed v5.1.0.
% 0.07/0.13 % Command : java -Dfile.encoding=UTF-8 -Xms512M -Xmx4G -Xss10M -jar /export/starexec/sandbox2/solver/bin/beagle.jar -auto -q -proof -print tff -smtsolver /export/starexec/sandbox2/solver/bin/cvc4-1.4-x86_64-linux-opt -liasolver cooper -t %d %s
% 0.13/0.34 % Computer : n023.cluster.edu
% 0.13/0.34 % Model : x86_64 x86_64
% 0.13/0.34 % CPU : Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
% 0.13/0.34 % Memory : 8042.1875MB
% 0.13/0.34 % OS : Linux 3.10.0-693.el7.x86_64
% 0.13/0.34 % CPULimit : 300
% 0.13/0.34 % WCLimit : 300
% 0.13/0.34 % DateTime : Thu Aug 3 17:53:52 EDT 2023
% 0.13/0.34 % CPUTime :
% 4.63/2.08 % SZS status Unsatisfiable for /export/starexec/sandbox2/benchmark/theBenchmark.p
% 4.63/2.09
% 4.63/2.09 % SZS output start CNFRefutation for /export/starexec/sandbox2/benchmark/theBenchmark.p
% See solution above
% 4.94/2.14
% 4.94/2.14 Inference rules
% 4.94/2.14 ----------------------
% 4.94/2.14 #Ref : 0
% 4.94/2.14 #Sup : 127
% 4.94/2.14 #Fact : 0
% 4.94/2.14 #Define : 0
% 4.94/2.14 #Split : 29
% 4.94/2.14 #Chain : 0
% 4.94/2.14 #Close : 0
% 4.94/2.14
% 4.94/2.14 Ordering : KBO
% 4.94/2.14
% 4.94/2.14 Simplification rules
% 4.94/2.14 ----------------------
% 4.94/2.14 #Subsume : 55
% 4.94/2.14 #Demod : 45
% 4.94/2.14 #Tautology : 42
% 4.94/2.14 #SimpNegUnit : 63
% 4.94/2.14 #BackRed : 24
% 4.94/2.14
% 4.94/2.14 #Partial instantiations: 0
% 4.94/2.14 #Strategies tried : 1
% 4.94/2.14
% 4.94/2.14 Timing (in seconds)
% 4.94/2.14 ----------------------
% 4.94/2.14 Preprocessing : 0.49
% 4.94/2.14 Parsing : 0.27
% 4.94/2.14 CNF conversion : 0.03
% 4.94/2.14 Main loop : 0.51
% 4.94/2.14 Inferencing : 0.17
% 4.94/2.14 Reduction : 0.15
% 4.94/2.14 Demodulation : 0.09
% 4.94/2.14 BG Simplification : 0.03
% 4.94/2.14 Subsumption : 0.11
% 4.94/2.14 Abstraction : 0.02
% 4.94/2.14 MUC search : 0.00
% 4.94/2.14 Cooper : 0.00
% 4.94/2.14 Total : 1.07
% 4.94/2.14 Index Insertion : 0.00
% 4.94/2.14 Index Deletion : 0.00
% 4.94/2.14 Index Matching : 0.00
% 4.94/2.14 BG Taut test : 0.00
%------------------------------------------------------------------------------