TSTP Solution File: PUZ019-1 by Otter---3.3
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- Process Solution
%------------------------------------------------------------------------------
% File : Otter---3.3
% Problem : PUZ019-1 : TPTP v8.1.0. Bugfixed v5.1.0.
% Transfm : none
% Format : tptp:raw
% Command : otter-tptp-script %s
% Computer : n014.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 : Wed Jul 27 13:11:23 EDT 2022
% Result : Unknown 85.20s 85.40s
% Output : None
% Verified :
% SZS Type : -
% Comments :
%------------------------------------------------------------------------------
%----No solution output by system
%------------------------------------------------------------------------------
%----ORIGINAL SYSTEM OUTPUT
% 0.07/0.12 % Problem : PUZ019-1 : TPTP v8.1.0. Bugfixed v5.1.0.
% 0.07/0.13 % Command : otter-tptp-script %s
% 0.13/0.34 % Computer : n014.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 : Wed Jul 27 01:49:09 EDT 2022
% 0.13/0.34 % CPUTime :
% 1.79/1.99 ----- Otter 3.3f, August 2004 -----
% 1.79/1.99 The process was started by sandbox2 on n014.cluster.edu,
% 1.79/1.99 Wed Jul 27 01:49:09 2022
% 1.79/1.99 The command was "./otter". The process ID is 26815.
% 1.79/1.99
% 1.79/1.99 set(prolog_style_variables).
% 1.79/1.99 set(auto).
% 1.79/1.99 dependent: set(auto1).
% 1.79/1.99 dependent: set(process_input).
% 1.79/1.99 dependent: clear(print_kept).
% 1.79/1.99 dependent: clear(print_new_demod).
% 1.79/1.99 dependent: clear(print_back_demod).
% 1.79/1.99 dependent: clear(print_back_sub).
% 1.79/1.99 dependent: set(control_memory).
% 1.79/1.99 dependent: assign(max_mem, 12000).
% 1.79/1.99 dependent: assign(pick_given_ratio, 4).
% 1.79/1.99 dependent: assign(stats_level, 1).
% 1.79/1.99 dependent: assign(max_seconds, 10800).
% 1.79/1.99 clear(print_given).
% 1.79/1.99
% 1.79/1.99 list(usable).
% 1.79/1.99 0 [] e_qual_people(X,X).
% 1.79/1.99 0 [] e_qual_jobs(X,X).
% 1.79/1.99 0 [] -e_qual_people(X,Y)|e_qual_people(Y,X).
% 1.79/1.99 0 [] -e_qual_jobs(X,Y)|e_qual_jobs(Y,X).
% 1.79/1.99 0 [] -e_qual_people(roberta,thelma).
% 1.79/1.99 0 [] -e_qual_people(roberta,pete).
% 1.79/1.99 0 [] -e_qual_people(roberta,steve).
% 1.79/1.99 0 [] -e_qual_people(pete,thelma).
% 1.79/1.99 0 [] -e_qual_people(pete,steve).
% 1.79/1.99 0 [] -e_qual_people(thelma,steve).
% 1.79/1.99 0 [] -e_qual_jobs(chef,guard).
% 1.79/1.99 0 [] -e_qual_jobs(chef,nurse).
% 1.79/1.99 0 [] -e_qual_jobs(chef,operator).
% 1.79/1.99 0 [] -e_qual_jobs(chef,police).
% 1.79/1.99 0 [] -e_qual_jobs(chef,actor).
% 1.79/1.99 0 [] -e_qual_jobs(chef,boxer).
% 1.79/1.99 0 [] -e_qual_jobs(chef,teacher).
% 1.79/1.99 0 [] -e_qual_jobs(guard,nurse).
% 1.79/1.99 0 [] -e_qual_jobs(guard,operator).
% 1.79/1.99 0 [] -e_qual_jobs(guard,police).
% 1.79/1.99 0 [] -e_qual_jobs(guard,actor).
% 1.79/1.99 0 [] -e_qual_jobs(guard,boxer).
% 1.79/1.99 0 [] -e_qual_jobs(guard,teacher).
% 1.79/1.99 0 [] -e_qual_jobs(nurse,operator).
% 1.79/1.99 0 [] -e_qual_jobs(nurse,police).
% 1.79/1.99 0 [] -e_qual_jobs(nurse,actor).
% 1.79/1.99 0 [] -e_qual_jobs(nurse,boxer).
% 1.79/1.99 0 [] -e_qual_jobs(nurse,teacher).
% 1.79/1.99 0 [] -e_qual_jobs(operator,police).
% 1.79/1.99 0 [] -e_qual_jobs(operator,actor).
% 1.79/1.99 0 [] -e_qual_jobs(operator,boxer).
% 1.79/1.99 0 [] -e_qual_jobs(operator,teacher).
% 1.79/1.99 0 [] -e_qual_jobs(police,actor).
% 1.79/1.99 0 [] -e_qual_jobs(police,boxer).
% 1.79/1.99 0 [] -e_qual_jobs(police,teacher).
% 1.79/1.99 0 [] -e_qual_jobs(actor,boxer).
% 1.79/1.99 0 [] -e_qual_jobs(actor,teacher).
% 1.79/1.99 0 [] -e_qual_jobs(boxer,teacher).
% 1.79/1.99 0 [] -has_job(X,nurse)|male(X).
% 1.79/1.99 0 [] -has_job(X,actor)|male(X).
% 1.79/1.99 0 [] -has_job(X,chef)|female(X).
% 1.79/1.99 0 [] -has_job(X,nurse)|educated(X).
% 1.79/1.99 0 [] -has_job(X,teacher)|educated(X).
% 1.79/1.99 0 [] -has_job(X,police)|educated(X).
% 1.79/1.99 0 [] -has_job(X,chef)| -has_job(X,police).
% 1.79/1.99 0 [] -male(X)| -female(X).
% 1.79/1.99 0 [] male(X)|female(X).
% 1.79/1.99 0 [] -husband(X,Y)|male(Y).
% 1.79/1.99 0 [] -husband(X,Y)|female(X).
% 1.79/1.99 0 [] -has_job(X,chef)| -has_job(Y,operator)|husband(X,Y).
% 1.79/1.99 0 [] -has_job(X,chef)|has_job(Y,operator)| -husband(X,Y).
% 1.79/1.99 0 [] -has_job(X,Z)| -has_job(Y,Z)|e_qual_people(X,Y).
% 1.79/1.99 0 [] -has_job(Z,U)| -has_job(Z,X)| -has_job(Z,Y)|e_qual_jobs(U,X)|e_qual_jobs(U,Y)|e_qual_jobs(X,Y).
% 1.79/1.99 0 [] has_job(roberta,X)|has_job(thelma,X)|has_job(pete,X)|has_job(steve,X).
% 1.79/1.99 0 [] has_job(X,chef)|has_job(X,guard)|has_job(X,nurse)|has_job(X,operator)|has_job(X,police)|has_job(X,teacher)|has_job(X,actor)|has_job(X,boxer).
% 1.79/1.99 0 [] -educated(pete).
% 1.79/1.99 0 [] -has_job(roberta,chef).
% 1.79/1.99 0 [] -has_job(roberta,boxer).
% 1.79/1.99 0 [] -has_job(roberta,police).
% 1.79/1.99 0 [] male(steve).
% 1.79/1.99 0 [] male(pete).
% 1.79/1.99 0 [] female(roberta).
% 1.79/1.99 0 [] female(thelma).
% 1.79/1.99 0 [] -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).
% 1.79/1.99 end_of_list.
% 1.79/1.99
% 1.79/1.99 SCAN INPUT: prop=0, horn=0, equality=0, symmetry=0, max_lits=8.
% 1.79/1.99
% 1.79/1.99 This is a non-Horn set without equality. The strategy will
% 1.79/1.99 be ordered hyper_res, unit deletion, and factoring, with
% 1.79/1.99 satellites in sos and with nuclei in usable.
% 1.79/1.99
% 1.79/1.99 dependent: set(hyper_res).
% 1.79/1.99 dependent: set(factor).
% 1.79/1.99 dependent: set(unit_deletion).
% 1.79/1.99
% 1.79/1.99 ------------> process usable:
% 1.79/1.99 ** KEPT (pick-wt=6): 1 [] -e_qual_people(A,B)|e_qual_people(B,A).
% 1.79/1.99 ** KEPT (pick-wt=6): 2 [] -e_qual_jobs(A,B)|e_qual_jobs(B,A).
% 1.79/1.99 ** KEPT (pick-wt=3): 3 [] -e_qual_people(roberta,thelma).
% 1.79/1.99 ** KEPT (pick-wt=3): 4 [] -e_qual_people(roberta,pete).
% 1.79/1.99 ** KEPT (pick-wt=3): 5 [] -e_qual_people(roberta,steve).
% 1.79/1.99 ** KEPT (pick-wt=3): 6 [] -e_qual_people(pete,thelma).
% 1.79/1.99 ** KEPT (pick-wt=3): 7 [] -e_qual_people(pete,steve).
% 1.79/1.99 ** KEPT (pick-wt=3): 8 [] -e_qual_people(thelma,steve).
% 1.79/1.99 ** KEPT (pick-wt=3): 9 [] -e_qual_jobs(chef,guard).
% 1.79/1.99 ** KEPT (pick-wt=3): 10 [] -e_qual_jobs(chef,nurse).
% 1.79/1.99 ** KEPT (pick-wt=3): 11 [] -e_qual_jobs(chef,operator).
% 85.20/85.40 ** KEPT (pick-wt=3): 12 [] -e_qual_jobs(chef,police).
% 85.20/85.40 ** KEPT (pick-wt=3): 13 [] -e_qual_jobs(chef,actor).
% 85.20/85.40 ** KEPT (pick-wt=3): 14 [] -e_qual_jobs(chef,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 15 [] -e_qual_jobs(chef,teacher).
% 85.20/85.40 ** KEPT (pick-wt=3): 16 [] -e_qual_jobs(guard,nurse).
% 85.20/85.40 ** KEPT (pick-wt=3): 17 [] -e_qual_jobs(guard,operator).
% 85.20/85.40 ** KEPT (pick-wt=3): 18 [] -e_qual_jobs(guard,police).
% 85.20/85.40 ** KEPT (pick-wt=3): 19 [] -e_qual_jobs(guard,actor).
% 85.20/85.40 ** KEPT (pick-wt=3): 20 [] -e_qual_jobs(guard,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 21 [] -e_qual_jobs(guard,teacher).
% 85.20/85.40 ** KEPT (pick-wt=3): 22 [] -e_qual_jobs(nurse,operator).
% 85.20/85.40 ** KEPT (pick-wt=3): 23 [] -e_qual_jobs(nurse,police).
% 85.20/85.40 ** KEPT (pick-wt=3): 24 [] -e_qual_jobs(nurse,actor).
% 85.20/85.40 ** KEPT (pick-wt=3): 25 [] -e_qual_jobs(nurse,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 26 [] -e_qual_jobs(nurse,teacher).
% 85.20/85.40 ** KEPT (pick-wt=3): 27 [] -e_qual_jobs(operator,police).
% 85.20/85.40 ** KEPT (pick-wt=3): 28 [] -e_qual_jobs(operator,actor).
% 85.20/85.40 ** KEPT (pick-wt=3): 29 [] -e_qual_jobs(operator,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 30 [] -e_qual_jobs(operator,teacher).
% 85.20/85.40 ** KEPT (pick-wt=3): 31 [] -e_qual_jobs(police,actor).
% 85.20/85.40 ** KEPT (pick-wt=3): 32 [] -e_qual_jobs(police,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 33 [] -e_qual_jobs(police,teacher).
% 85.20/85.40 ** KEPT (pick-wt=3): 34 [] -e_qual_jobs(actor,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 35 [] -e_qual_jobs(actor,teacher).
% 85.20/85.40 ** KEPT (pick-wt=3): 36 [] -e_qual_jobs(boxer,teacher).
% 85.20/85.40 ** KEPT (pick-wt=5): 37 [] -has_job(A,nurse)|male(A).
% 85.20/85.40 ** KEPT (pick-wt=5): 38 [] -has_job(A,actor)|male(A).
% 85.20/85.40 ** KEPT (pick-wt=5): 39 [] -has_job(A,chef)|female(A).
% 85.20/85.40 ** KEPT (pick-wt=5): 40 [] -has_job(A,nurse)|educated(A).
% 85.20/85.40 ** KEPT (pick-wt=5): 41 [] -has_job(A,teacher)|educated(A).
% 85.20/85.40 ** KEPT (pick-wt=5): 42 [] -has_job(A,police)|educated(A).
% 85.20/85.40 ** KEPT (pick-wt=6): 43 [] -has_job(A,chef)| -has_job(A,police).
% 85.20/85.40 ** KEPT (pick-wt=4): 44 [] -male(A)| -female(A).
% 85.20/85.40 ** KEPT (pick-wt=5): 45 [] -husband(A,B)|male(B).
% 85.20/85.40 ** KEPT (pick-wt=5): 46 [] -husband(A,B)|female(A).
% 85.20/85.40 ** KEPT (pick-wt=9): 47 [] -has_job(A,chef)| -has_job(B,operator)|husband(A,B).
% 85.20/85.40 ** KEPT (pick-wt=9): 48 [] -has_job(A,chef)|has_job(B,operator)| -husband(A,B).
% 85.20/85.40 ** KEPT (pick-wt=9): 49 [] -has_job(A,B)| -has_job(C,B)|e_qual_people(A,C).
% 85.20/85.40 ** KEPT (pick-wt=18): 50 [] -has_job(A,B)| -has_job(A,C)| -has_job(A,D)|e_qual_jobs(B,C)|e_qual_jobs(B,D)|e_qual_jobs(C,D).
% 85.20/85.40 ** KEPT (pick-wt=2): 51 [] -educated(pete).
% 85.20/85.40 ** KEPT (pick-wt=3): 52 [] -has_job(roberta,chef).
% 85.20/85.40 ** KEPT (pick-wt=3): 53 [] -has_job(roberta,boxer).
% 85.20/85.40 ** KEPT (pick-wt=3): 54 [] -has_job(roberta,police).
% 85.20/85.40 ** KEPT (pick-wt=24): 55 [] -has_job(A,chef)| -has_job(B,guard)| -has_job(C,nurse)| -has_job(D,operator)| -has_job(E,police)| -has_job(F,teacher)| -has_job(G,actor)| -has_job(H,boxer).
% 85.20/85.40
% 85.20/85.40 ------------> process sos:
% 85.20/85.40 ** KEPT (pick-wt=3): 58 [] e_qual_people(A,A).
% 85.20/85.40 ** KEPT (pick-wt=3): 59 [] e_qual_jobs(A,A).
% 85.20/85.40 ** KEPT (pick-wt=4): 60 [] male(A)|female(A).
% 85.20/85.40 ** KEPT (pick-wt=12): 61 [] has_job(roberta,A)|has_job(thelma,A)|has_job(pete,A)|has_job(steve,A).
% 85.20/85.40 ** KEPT (pick-wt=24): 62 [] has_job(A,chef)|has_job(A,guard)|has_job(A,nurse)|has_job(A,operator)|has_job(A,police)|has_job(A,teacher)|has_job(A,actor)|has_job(A,boxer).
% 85.20/85.40 ** KEPT (pick-wt=2): 63 [] male(steve).
% 85.20/85.40 ** KEPT (pick-wt=2): 64 [] male(pete).
% 85.20/85.40 ** KEPT (pick-wt=2): 65 [] female(roberta).
% 85.20/85.40 ** KEPT (pick-wt=2): 66 [] female(thelma).
% 85.20/85.40 58 back subsumes 56.
% 85.20/85.40 59 back subsumes 57.
% 85.20/85.40
% 85.20/85.40 ======= end of input processing =======
% 85.20/85.40
% 85.20/85.40 =========== start of search ===========
% 85.20/85.40
% 85.20/85.40 Search stopped in tp_alloc by max_mem option.
% 85.20/85.40
% 85.20/85.40 Search stopped in tp_alloc by max_mem option.
% 85.20/85.40
% 85.20/85.40 ============ end of search ============
% 85.20/85.40
% 85.20/85.40 -------------- statistics -------------
% 85.20/85.40 clauses given 8
% 85.20/85.40 clauses generated 9526
% 85.20/85.40 clauses kept 5171
% 85.20/85.40 clauses forward subsumed 4419
% 85.20/85.40 clauses back subsumed 2
% 85.20/85.40 Kbytes malloced 11718
% 85.20/85.40
% 85.20/85.40 ----------- times (seconds) -----------
% 85.20/85.40 user CPU time 83.40 (0 hr, 1 min, 23 sec)
% 85.20/85.40 system CPU time 0.01 (0 hr, 0 min, 0 sec)
% 85.20/85.40 wall-clock time 85 (0 hr, 1 min, 25 sec)
% 85.20/85.40
% 85.20/85.40 Process 26815 finished Wed Jul 27 01:50:34 2022
% 85.20/85.40 Otter interrupted
% 85.20/85.40 PROOF NOT FOUND
%------------------------------------------------------------------------------