;;; TERRORIST Story from WSJ
;;;
;;; This example demonstrates EPILOG's Yes/No and WH-question answering
;;; capability, in addition to its forward inferencing capability.
;;;
;;; The input story for this example is the following sentence from Wall
;;; Street Journal: "A bomb exploded in the office of an [Afghan] guerrilla
;;; group in a crowded shiite moslem neighborhood in Beirut."
;;; The input file contains a meaning postulate, 12 pieces of world knowledge,
;;; and the above story in Episodic Logic form, followed by a list of
;;; Y/N-questions and then WH-questions that test whether EPILOG understood
;;; the above story.
;;;
;;; The output file shows the forward inferences generated by EPILOG
;;; while processing the story and the question-answering session.
;;;=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
;;; INPUT FILE
;;;=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
(unless (find-package "EPILOG")
(load "/home/diamond/u5/epilog/EPILOG4.3/epi/epi"))
(in-package #+lucid 'epilog #-lucid epilog)
(use-spec 'time-specialist)
(add-lex 'Beirut 'name :trans "Beirut" :subject-pronoun "it"
:object-pronoun "it")
(trace 'forward-details)
(checkpoint 'one)
;Meaning postulate
;If there is a collection of some type, the collection has at least one member.
;Furthermore, every member of the collection is of that type.
(mp '(A x_pred (A y_term ((qq (y (coll x))) true)
((y set) and
((qq (E z (z member-of y))) true)
((qq (A w (w member-of y) (w x))) true))))
)
;World knowledge
(kn
;K1. If a car is explosives-laden, then that car is a car bomb.
; Without this we can't really do too much!
'(A x1 (x1 car)
(A e1_ep ((x1 explosives-laden) * e1) (x1 carbomb)))
;K2. Given two locations, if the first location is in the second, and an event
; occurred near the first location, then that event occurred in the second.
'((E x (x location)
(E y ((y location) and (x in y))
(E z (z occurred-near x)))) implies (z occurred-in y))
;K3. If something kills something else, then the killed something is dead.
'(A e1_ep
(A x1 (x1 thing)
(A x2 ((x2 thing) and ((x2 kill x1) * e1))
(E e2 ((e2 episode) and (e1 cause-of e2)) ((x1 dead) ** e2)))))
'(A x (x episode)
(A y ((y episode) and (y cause-of x)) (x after-0 y)))
;K4. Anything in a location that has a bomb explode in it is probably
; destroyed.
'((E x1 (x1 location)
(E x2 (x2 physical-object)
(E e1_ep ((x2 in x1) ** e1)
(E e2_ep ((e2 during e1) and (e2 occurred-near x1))
(E x3 (x3 bomb) ((x3 explode) ** e2) )))))
0.9 (E e3 ((e3 episode) and (e2 cause-of e3))
((x2 destroyed) ** e3)))
;K5. Any bomb that blows up is destroyed as well.
'((E x1 (x1 bomb) (E e1_ep ((x1 explode) * e1)))
0.99 (E e2 ((e2 episode) and (e1 cause-of e2))
((x1 destroyed) ** e2)))
;K6. Anyone that is "destroyed" is killed.
'(A x1 (x1 human)
(A e1_ep ((x1 destroyed) * e1)
(E e2 ((e2 episode) and (e2 cause-of e1)
(E x3 (x3 thing) ((x3 kill x1) ** e2))))))
;K7. If a location is crowded then there must be at least one person in it.
'(A x1 ((x1 location) and (E e1 (e1 episode) ((x1 crowded) @ e1)))
(E x2_set (x2 (coll human))
(A y (y member-of x2)
(E e2 ((e2 episode) and (e2 same-time e1))
((y in x1) ** e2)))))
;K8. If an incident occurs in which a bomb blows up in a neighborhood,
; it is a terrorist event.
'(A e1_ep
(A x1 (x1 bomb)
(A x2 ((x2 neighborhood) and (e1 occurred-in x2) ((x1 explode) * e1))
(e1 terrorist-incident))))
;K9. If a location is full of something, at least one of that someting
; is in the location.
'(A x1 (x1 location)
(A x2_set ;(x2 (coll physical-object))
(A e1_ep ((x1 full-of x2) * e1)
(E e2 ((e2 episode) and (e2 same-time e1)) ;((x2 in x1) ** e2)
(A x3 (x3 member-of x2) ((x3 in x1) ** e2))))))
;K10. If a bomb blows up near a location, there is some chance that the
; location (or part of it) is destroyed.
'((E x1 (x1 bomb)
(E x2 (x2 location)
(E e1_ep (((x1 explode) ** e1) and (e1 occurred-near x2)))))
0.5 (E e2 ((e2 episode) and (e1 cause-of e2)) ((x2 damaged) ** e2)))
;K11. If something is an office, then there is a good chance that
; there is furniture, document, or computers in it.
; (Need to use "lifetimes" for this.)
'((E x (E y_ep ((x office) * y)))
0.5 (E z (z (coll furniture))
(E e1 ((e1 episode) and (e1 same-time y))
((x full-of z) ** e1))))
'((E x (E y_ep ((x office) * y)))
0.5 (E e1 ((e1 episode) and (e1 same-time y))
(E z (z (coll document))
(E x1 (x1 (coll computer))
(((x full-of z) and (x full-of x1)) ** e1)))))
;K12. If something is a location at a specific time, it is always a location.
'(A x (A y_ep ((x location) * y) (x location)))
'(A x (A y_ep ((x office) * y) (x location)))
)
;Show all EPILOG knows about 'office!
(display 'office)
(checkpoint 'start)
(story
;Beirut is a city
'(Beirut city)
;A bomb exploded in the office of an [Afghan] guerrilla group in a
;crowded shiite moslem neighborhood in Beirut.
'(E sx1 ((sx1 neighborhood) and (sx1 shiite-moslem) (sx1 in Beirut)
(E se1 ((se1 episode) and ((sx1 crowded) ** se1))))
(E sx2 ((E se2 ((se2 episode) and (se2 during se1))
((sx2 office) ** se2)) and
(sx2 in sx1)
(E sx3 (sx3 (coll guerilla)) (sx2 belong-to sx3)))
(E sx4 ((sx4 car) and
(E se3 ((se3 episode) and (se3 during se2))
((sx4 explosives-laden) ** se3)))
(E se4 ((se4 episode) and (se4 after-0 se3) (se4 during se2)
((sx4 blow-up) ** se4))
(se4 occurred-near sx2))))
)
)
; epilog should infer: office contents: furniture, documents, computers;
; should infer all contents are destroyed, office damaged, bomb destroyed.
(write-perm-memory "junk")
(tweak '*minimum-effort* 0)
;Was a bomb destroyed? (should be YES)
(q '(e x (x bomb) (e y_episode ((x destroyed) * y))))
;Was an office destroyed? (should be YES)
(q '(e x (e e1 ((x office) * e1)) (e y_episode ((x damaged) * y))))
(q '(e x ((e e1 ((x office) * e1)) and (e y_episode ((x damaged) * y)))))
;Same question rephrased with more info. Cannot try with thing instead of
;location though, because office is not on the thing hierarchy.
(q '(e x (x location)
((e e1 ((x office) * e1)) and (e y_episode ((x damaged) * y)))))
;Was furniture destroyed? (should be YES)
(q '(e x (x furniture) (e y_episode ((x destroyed) * y))))
;Did furniture blow up? (should be UNKNOWN)
(q '(e x (x furniture) (e y_episode ((x blow-up) * y))))
;Was there an office full of furniture? (should be YES)
(q '(e x (x (coll furniture))
(e y (e e1 ((y office) * e1))
(e z_episode ((y full-of x) ** z)))))
;Was someone dead?
(q '(e x (x human) (e y_episode ((x dead) * y)))) ;yes
;Was a car destroyed?
(q '(e x (x car) (e z_episode ((x destroyed) * z)))) ;yes
;Did something blow up?
(q '(e x (x physical-object) (e y ((x blow-up) * y)))) ;yes
;Was there something near the place where explosion took place?
(q '(e x (x physical-object)
(e y (y location)
(e z_episode (z occurred-near y) ((x explode) * z) )))) ;yes
;Same question with two literals interchanged.
;We prefer the first version, especially if we go to the new probability
;interpretation where probs go on the main part of a e x ... proposition.
(q '(e x (x physical-object)
(e y (y location)
(e z_episode ((x explode) * z) (z occurred-near y) ))))
(q '(e x (x physical-object) (e y ((x in sx1) ** y)))) ;yes
(q '(e x (x physical-object) (e y ((x destroyed) ** y)))) ;yes
(q '(e x (x computer) (e y ((x destroyed) ** y)))) ;yes
(q '(e x (x (coll physical-object))
(e y ((sx2 full-of x) @ y) (y same-time se2))))
(q '(e x (x (coll physical-object))
(e y (y same-time se2) ((sx2 full-of x) @ y) )))
(q '(e x ((x physical-object) and (e y ((x in sx2) @ y) (y during se2)))
(e z ((x destroyed) ** z)))) ;-
(q '(e x ((x physical-object) and (e y (y during se2) ((x in sx2) @ y) ))
(e z ((x destroyed) ** z))))
(q '(e x ((x physical-object) and (e y (se4 during y) ((x in sx2) @ y) ))
(e z ((x destroyed) ** z))))
(q '(e x (x physical-object) ((e y (y during se2) ((x in sx2) @ y) ) and
(e z ((x destroyed) ** z)))))
;The following questions should be answered NO!
;Was no computer destroyed?
(q '(a x ((x computer) and (e e1 (e1 during se2) ((x in sx2) * e1)))
(not e y ((x destroyed) * y))))
(q '(a x (x computer) (a e1 ((e1 during se2) and ((x in sx2) * e1))
(not e y ((x destroyed) * y)))))
(q '((e x (x computer) (e e1 ((e1 during se2) and ((x in sx2) * e1))))
0.7 (not e y ((x destroyed) * y))))
(q '((e x (x computer) (e e1 ((e1 during se2) and ((x in sx2) * e1))))
0.4 (not e y ((x destroyed) * y))))
(q '((e x (x computer) (e e1 ((e1 during se2) and ((x in sx2) * e1))))
implies (not e y ((x destroyed) * y))))
(tweak '*spec-compare-lits* t)
(q '(a x ((x physical-object) and
(e y ((y episode) and (y during se2)) ((x in sx2) ** y) ))
(e z ((x destroyed) ** z))))
(tweak '*spec-compare-lits*)
(q '(a x ((x physical-object) and
(e y ((y episode) and (se4 during y)) ((x in sx2) ** y) ))
(e z ((x destroyed) ** z))))
(tweak '*goal-forward* t)
(tweak '*spec-compare-lits* t)
(q '(a x ((x physical-object) and
(e y ((y episode) and (y during se2)) ((x in sx2) ** y) ))
(e z ((x destroyed) ** z))))
(tweak '*spec-compare-lits*)
(q '(a x ((x physical-object) and
(e y ((y episode) and (se4 during y)) ((x in sx2) ** y) ))
(e z ((x destroyed) ** z))))
(tweak '*goal-forward*)
(q '(a x (x computer) (e y (y episode) ((x destroyed) ** y)))) ;unknown
;What was destroyed?
(q '(WH x (E y ((x destroyed) @ y))) 1)
(q '(WH x (E y ((x destroyed) @ y))) nil 1)
;What was damaged?
(q '(WH x (E y ((x damaged) @ y))))
(tweak '*max-wh-difference* 3)
(q '(WH x (E y ((x damaged) @ y))))
(tweak '*max-wh-difference*)
;What exploded?
(q '(WH x (E y ((x explode) @ y))))
;Who killed whom?
(q '(WH x (WH y (E z ((x kill y) @ z)))))
;What was the office full of?
(q '(WH x (E y ((sx2 full-of x) @ y))))
;What was where?
(q '(WH x (WH y (E z ((x in y) @ z)))))
;Who was dead?
(q '(WH x (E y ((x dead) @ y))))
(tweak '*qa-iterations*)
(untrace 'forward-details)
;;;=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
;;; OUTPUT
;;;=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
; Loading /usr/staff/lib/cl/siteinit.cl
; Loading sitewide siteinit.cl file.
;;; defined (reset-default-opt) to change optimization settings back to defaults:
(declaim (optimize (space 1) (speed 2) (safety 1) (debug 2)))
;; Optimization settings: safety 1, space 1, speed 2, debug 2.
;; For a complete description of all compiler switches given the current
;; optimization settings evaluate (EXPLAIN-COMPILER-SETTINGS).
--> Running Cold Initializations
--> Running Warm Initializations
Initing *random-state*
CL-LIB Version: 4.11 Time-stamp: <96/04/17 20:09:03 miller>
USER(1): ; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/epi.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/EPILOG.fasl43
******************************************************************
* Welcome to EPILOG: the computational system for Episodic Logic *
* *
* Last update: August 1993 *
******************************************************************
This version of EPILOG is copyrighted by the University of Alberta.
It may be used for academic and instructional purposes, but not for
commercial purposes (except by Boeing Company); publication of the
source requires written permission from Boeing, for five years
following acceptance of this version of EPILOG by Boeing.
MEMORY MANAGEMENT .....
LOADING EPILOG .....
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; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/spec/spec.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/spec/interface.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/set-equal/defs.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/set-equal/general.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/set-equal/enter.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/equal/functions.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/equal/eval.fasl43
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; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/equal/print.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/equal/compare.fasl43
Specialist EQUALITY-SPECIALIST is now activeDONE use-spec specialists
; Loading /home/diamond/u5/epilog/EPILOG4.3/epi/epi-hier
DONE load epi-hier
; Loading /home/diamond/u5/epilog/EPILOG4.3/epi/epi-indicate
DONE load epi-indicate
; Loading /home/diamond/u5/epilog/EPILOG4.3/epi/epi-init.lisp
DONE load epi/epi-init.lisp
; Loading ./epi-init.lisp
; Loading ./eg.hier
; Loading ./eg.indicate
; Loading ./eg.parts
; Loading ./eg.trans
; Loading ./eg.lex
DONE load epi-init.lisp DONE number-hier DONE loading EPILOG
T
USER(2): #
EPI(3): EPI(3):
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/definition.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/time.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/spec/spec.fasl43
; Fast loading
; /home/diamond/u5/epilog/EPILOG4.3/epi/spec/interface.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/pred.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/defs.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/general.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/psuedo.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/absdur.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/links.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/find.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/eval.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/enter.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/print.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/compare.fasl43
; Fast loading /home/diamond/u5/epilog/EPILOG4.3/epi/time/functions.fasl43
Specialist TIME-SPECIALIST is now active
(TIME-SPECIALIST)
EPI(4): NIL
EPI(5): NIL
EPI(6): NIL
EPI(7): EPI(7):
Loading meaning postulate ...
WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND ((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE (A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X))) TRUE))))
Probability: 1
(WFF16)
EPI(8): EPI(8):
Loading knowledge ...
WFF22: (A X (X CAR) (A EP-Y ((X EXPLOSIVES-LADEN) * EP-Y) (X CARBOMB)))
Probability: 1
Cars that are laden with explosives are carbombs.
Loading knowledge ...
WFF32: ((E X (X LOCATION)
(E Y ((Y LOCATION) AND (X IN Y)) (E Z (Z OCCURRED-NEAR X))))
IMPLIES (Z OCCURRED-IN Y))
Probability: 1
If something is near a location in another location, it is in that other location.
Loading knowledge ...
WFF46: (A EP-X
(A Y (Y THING)
(A Z ((Z THING) AND ((Z KILL Y) * EP-X))
(E U ((U EPISODE) AND (EP-X CAUSE-OF U)) ((Y DEAD) ** U)))))
Probability: 1
WARNING: Response: Missing translation information for DEAD - guessing
Anything that is killed by another thing is dead because the other thing kills it.
Loading knowledge ...
WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X)) (X AFTER-0 Y)))
Probability: 1
If an episode is because another episode, it is after that other episode.
Loading knowledge ...
WFF73: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
(E EP-X1 ((EP-X1 DURING EP-Z) AND (EP-X1 OCCURRED-NEAR X))
(E Y1 (Y1 BOMB) ((Y1 EXPLODE) ** EP-X1))))))
0.9
(E U ((U EPISODE) AND (EP-X1 CAUSE-OF U)) ((Y DESTROYED) ** U)))
Probability: 1
If a bomb explodes near a location while a physical object is in the location, the object is usually destroyed.
Loading knowledge ...
WFF84: ((E X (X BOMB) (E EP-Y ((X EXPLODE) * EP-Y))) 0.99
(E U ((U EPISODE) AND (EP-Y CAUSE-OF U)) ((X DESTROYED) ** U)))
Probability: 1
Any bomb that explodes is usually destroyed.
Loading knowledge ...
WFF95: (A X (X HUMAN)
(A EP-Y ((X DESTROYED) * EP-Y)
(E U
((U EPISODE) AND (U CAUSE-OF EP-Y)
(E V (V THING) ((V KILL X) ** U))))))
Probability: 1
Everyone is destroyed because he is killed by something.
Loading knowledge ...
WFF110: (A X ((X LOCATION) AND (E Y (Y EPISODE) ((X CROWDED) @ Y)))
(E SET-U (SET-U (COLL HUMAN))
(A Z (Z MEMBER-OF SET-U)
(E V ((V EPISODE) AND (V SAME-TIME Y)) ((Z IN X) ** V)))))
Probability: 1
WARNING: Response: Missing translation information for CROWDED - guessing
If a location is crowded, a group of humans is in it at the same time.
Loading knowledge ...
WFF120: (A EP-X
(A Y (Y BOMB)
(A Z
((Z NEIGHBORHOOD) AND (EP-X OCCURRED-IN Z)
((Y EXPLODE) * EP-X))
(EP-X TERRORIST-INCIDENT))))
Probability: 1
If a bomb explodes in a neighborhood, it is terrorist.
Loading knowledge ...
WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
Probability: 1
If a location is full of a set, the set are in it at the same time.
Loading knowledge ...
WFF144: ((E X (X BOMB)
(E Y (Y LOCATION)
(E EP-Z ((EP-Z OCCURRED-NEAR Y) AND ((X EXPLODE) ** EP-Z)))))
0.5 (E U ((U EPISODE) AND (EP-Z CAUSE-OF U)) ((Y DAMAGED) ** U)))
Probability: 1
WARNING: Response: Missing translation information for DAMAGED - guessing
If a bomb explodes near a location, it is likely damaged.
Loading knowledge ...
WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V))))
Probability: 1
Any office that is an office is likely full of some furniture at the same time.
Loading knowledge ...
WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
Probability: 1
Any office full of documents that is an office is likely full of computers at the same time.
Loading knowledge ...
WFF171: (A X (A EP-Y ((X LOCATION) * EP-Y) (X LOCATION)))
Probability: 1
.
Loading knowledge ...
WFF173: (A X (A EP-Y ((X OFFICE) * EP-Y) (X LOCATION)))
Probability: 1
Offices that are offices are locations.
(WFF22 WFF32 WFF46 WFF53 WFF73 WFF84 WFF95 WFF110 WFF120 WFF132 WFF144 WFF156
WFF168 WFF171 WFF173)
EPI(9):
OFFICE
TP.TYPE
WFF173: (A X (A EP-Y ((X OFFICE) * EP-Y) (X LOCATION))) Probability: 1
WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V)))) Probability: 1
WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U))))) Probability: 1
TP.STATIC-RELATIONSHIP
TP.PHYSICAL-RELATIONSHIP
TP.CONTAINMENT
WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V)))) Probability: 1
NIL
EPI(10): EPI(10): NIL
EPI(11):
Loading story sentence ...
WFF174: (BEIRUT CITY)
Stored in Input Array [0]
Probability: 1
Beirut is a city.
Loading story sentence ...
WFF175: (C1 NEIGHBORHOOD)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF176: (C1 SHIITE-MOSLEM)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF177: (C1 IN BEIRUT)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF178: (C2 EPISODE)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF180: ((C1 CROWDED) ** C2)
Stored in Input Array [1]
Probability: 1
** INFERRED FORMULA **
WFF190: (SET-C3 (COLL HUMAN))
Rank: 1
Parents: WFF110: (A X
((X LOCATION) AND
(E Y (Y EPISODE) ((X CROWDED) @ Y)))
(E SET-U (SET-U (COLL HUMAN))
(A Z (Z MEMBER-OF SET-U)
(E V ((V EPISODE) AND (V SAME-TIME Y))
((Z IN X) ** V)))))
WFF178: (C2 EPISODE)
WFF175: (C1 NEIGHBORHOOD)
WFF180: ((C1 CROWDED) ** C2)
Probability: 1
** INFERRED FORMULA **
WFF191: (SET-C3 SET)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF190: (SET-C3 (COLL HUMAN))
Probability: 1
** INFERRED FORMULA **
WFF197: (C4 MEMBER-OF SET-C3)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF190: (SET-C3 (COLL HUMAN))
Probability: 1
** INFERRED FORMULA **
WFF195: (A X (X MEMBER-OF SET-C3) (X HUMAN))
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF190: (SET-C3 (COLL HUMAN))
Probability: 1
** INFERRED FORMULA **
WFF198: (C4 HUMAN)
Rank: 3
Parents: WFF195: (A X (X MEMBER-OF SET-C3) (X HUMAN))
WFF197: (C4 MEMBER-OF SET-C3)
Probability: 1
** INFERRED FORMULA **
WFF204: (A X (X MEMBER-OF SET-C3)
(E EP-U ((EP-U EPISODE) AND (EP-U SAME-TIME C2))
((X IN C1) ** EP-U)))
Rank: 1
Parents: WFF110: (A X
((X LOCATION) AND
(E Y (Y EPISODE) ((X CROWDED) @ Y)))
(E SET-U (SET-U (COLL HUMAN))
(A Z (Z MEMBER-OF SET-U)
(E V ((V EPISODE) AND (V SAME-TIME Y))
((Z IN X) ** V)))))
WFF178: (C2 EPISODE)
WFF175: (C1 NEIGHBORHOOD)
WFF180: ((C1 CROWDED) ** C2)
Probability: 1
** INFERRED FORMULA **
WFF208: (EP-C5 EPISODE)
Rank: 3
Parents: WFF204: (A X (X MEMBER-OF SET-C3)
(E EP-U ((EP-U EPISODE) AND (EP-U SAME-TIME C2))
((X IN C1) ** EP-U)))
WFF197: (C4 MEMBER-OF SET-C3)
Probability: 1
** INFERRED FORMULA **
WFF209: (EP-C5 SAME-TIME C2)
Rank: 3
Parents: WFF204: (A X (X MEMBER-OF SET-C3)
(E EP-U ((EP-U EPISODE) AND (EP-U SAME-TIME C2))
((X IN C1) ** EP-U)))
WFF197: (C4 MEMBER-OF SET-C3)
Probability: 1
** INFERRED FORMULA **
WFF210: ((C4 IN C1) ** EP-C5)
Rank: 3
Parents: WFF204: (A X (X MEMBER-OF SET-C3)
(E EP-U ((EP-U EPISODE) AND (EP-U SAME-TIME C2))
((X IN C1) ** EP-U)))
WFF197: (C4 MEMBER-OF SET-C3)
Probability: 1
Loading story sentence ...
WFF211: (C7 EPISODE)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF212: (C7 DURING C2)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF214: ((C6 OFFICE) ** C7)
Stored in Input Array [1]
Probability: 1
** INFERRED FORMULA **
WFF215: (C6 LOCATION)
Rank: 1
Parents: WFF173: (A X (A EP-Y ((X OFFICE) * EP-Y) (X LOCATION)))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF222: (C8 (COLL FURNITURE))
Rank: 1
Parents: WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF223: (C8 SET)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF222: (C8 (COLL FURNITURE))
Probability: 1
** INFERRED FORMULA **
WFF230: (C9 MEMBER-OF C8)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF222: (C8 (COLL FURNITURE))
Probability: 1
** INFERRED FORMULA **
WFF228: (A X (X MEMBER-OF C8) (X FURNITURE))
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF222: (C8 (COLL FURNITURE))
Probability: 1
** INFERRED FORMULA **
WFF231: (C9 FURNITURE)
Rank: 3
Parents: WFF228: (A X (X MEMBER-OF C8) (X FURNITURE))
WFF230: (C9 MEMBER-OF C8)
Probability: 1
** INFERRED FORMULA **
WFF237: (EP-C10 EPISODE)
Rank: 1
Parents: WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF238: (EP-C10 SAME-TIME C7)
Rank: 1
Parents: WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF239: ((C6 FULL-OF C8) ** EP-C10)
Rank: 1
Parents: WFF156: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U (U (COLL FURNITURE))
(E V ((V EPISODE) AND (V SAME-TIME EP-Y))
((X FULL-OF U) ** V))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 0.5
** INFERRED FORMULA **
WFF246: (EP-C11 EPISODE)
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF239: ((C6 FULL-OF C8) ** EP-C10)
Probability: 1
** INFERRED FORMULA **
WFF247: (EP-C11 SAME-TIME EP-C10)
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF239: ((C6 FULL-OF C8) ** EP-C10)
Probability: 1
** INFERRED FORMULA **
WFF249: (A X (X MEMBER-OF C8) ((X IN C6) ** EP-C11))
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF239: ((C6 FULL-OF C8) ** EP-C10)
Probability: 0.5
** INFERRED FORMULA **
WFF251: ((C9 IN C6) ** EP-C11)
Rank: 3
Parents: WFF249: (A X (X MEMBER-OF C8) ((X IN C6) ** EP-C11))
WFF230: (C9 MEMBER-OF C8)
Probability: 0.5
** INFERRED FORMULA **
WFF259: (EP-C12 EPISODE)
Rank: 1
Parents: WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF260: (EP-C12 SAME-TIME C7)
Rank: 1
Parents: WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF266: (C13 (COLL DOCUMENT))
Rank: 1
Parents: WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF267: (C13 SET)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF266: (C13 (COLL DOCUMENT))
Probability: 1
** INFERRED FORMULA **
WFF274: (C14 MEMBER-OF C13)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF266: (C13 (COLL DOCUMENT))
Probability: 1
** INFERRED FORMULA **
WFF272: (A X (X MEMBER-OF C13) (X DOCUMENT))
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF266: (C13 (COLL DOCUMENT))
Probability: 1
** INFERRED FORMULA **
WFF275: (C14 DOCUMENT)
Rank: 3
Parents: WFF272: (A X (X MEMBER-OF C13) (X DOCUMENT))
WFF274: (C14 MEMBER-OF C13)
Probability: 1
** INFERRED FORMULA **
WFF281: (C15 (COLL COMPUTER))
Rank: 1
Parents: WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 1
** INFERRED FORMULA **
WFF282: (C15 SET)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF281: (C15 (COLL COMPUTER))
Probability: 1
** INFERRED FORMULA **
WFF289: (C16 MEMBER-OF C15)
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF281: (C15 (COLL COMPUTER))
Probability: 1
** INFERRED FORMULA **
WFF287: (A X (X MEMBER-OF C15) (X COMPUTER))
Rank: 2
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE
(A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF281: (C15 (COLL COMPUTER))
Probability: 1
** INFERRED FORMULA **
WFF290: (C16 COMPUTER)
Rank: 3
Parents: WFF287: (A X (X MEMBER-OF C15) (X COMPUTER))
WFF289: (C16 MEMBER-OF C15)
Probability: 1
** INFERRED FORMULA **
WFF291: ((C6 FULL-OF C13) * EP-C12)
Rank: 1
Parents: WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 0.5
** INFERRED FORMULA **
WFF296: (EP-C17 EPISODE)
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF291: ((C6 FULL-OF C13) * EP-C12)
Probability: 1
** INFERRED FORMULA **
WFF297: (EP-C17 SAME-TIME EP-C12)
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF291: ((C6 FULL-OF C13) * EP-C12)
Probability: 1
** INFERRED FORMULA **
WFF299: (A X (X MEMBER-OF C13) ((X IN C6) ** EP-C17))
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF291: ((C6 FULL-OF C13) * EP-C12)
Probability: 0.5
** INFERRED FORMULA **
WFF301: ((C14 IN C6) ** EP-C17)
Rank: 3
Parents: WFF299: (A X (X MEMBER-OF C13) ((X IN C6) ** EP-C17))
WFF274: (C14 MEMBER-OF C13)
Probability: 0.5
** INFERRED FORMULA **
WFF303: ((C6 FULL-OF C15) * EP-C12)
Rank: 1
Parents: WFF168: ((E X (E EP-Y ((X OFFICE) * EP-Y))) 0.5
(E U ((U EPISODE) AND (U SAME-TIME EP-Y))
(E V (V (COLL DOCUMENT))
(E W (W (COLL COMPUTER))
(((X FULL-OF V) AND (X FULL-OF W)) ** U)))))
WFF214: ((C6 OFFICE) ** C7)
Probability: 0.5
** INFERRED FORMULA **
WFF306: (EP-C18 EPISODE)
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF303: ((C6 FULL-OF C15) * EP-C12)
Probability: 1
** INFERRED FORMULA **
WFF307: (EP-C18 SAME-TIME EP-C12)
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF303: ((C6 FULL-OF C15) * EP-C12)
Probability: 1
** INFERRED FORMULA **
WFF309: (A X (X MEMBER-OF C15) ((X IN C6) ** EP-C18))
Rank: 2
Parents: WFF132: (A X (X LOCATION)
(A SET-Y
(A EP-Z ((X FULL-OF SET-Y) * EP-Z)
(E U ((U EPISODE) AND (U SAME-TIME EP-Z))
(A Y1 (Y1 MEMBER-OF SET-Y) ((Y1 IN X) ** U))))))
WFF215: (C6 LOCATION)
WFF303: ((C6 FULL-OF C15) * EP-C12)
Probability: 0.5
** INFERRED FORMULA **
WFF311: ((C16 IN C6) ** EP-C18)
Rank: 3
Parents: WFF309: (A X (X MEMBER-OF C15) ((X IN C6) ** EP-C18))
WFF289: (C16 MEMBER-OF C15)
Probability: 0.5
Loading story sentence ...
WFF312: (C6 IN C1)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF313: (C19 (COLL GUERILLA))
Stored in Input Array [1]
Probability: 1
** INFERRED FORMULA **
WFF314: (C19 SET)
Rank: 1
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE (A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF313: (C19 (COLL GUERILLA))
Probability: 1
** INFERRED FORMULA **
WFF321: (C20 MEMBER-OF C19)
Rank: 1
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE (A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF313: (C19 (COLL GUERILLA))
Probability: 1
** INFERRED FORMULA **
WFF319: (A X (X MEMBER-OF C19) (X GUERILLA))
Rank: 1
Parents: WFF16: (A PRED-X
(A TERM-Y ((QQUOTE (TERM-Y (COLL PRED-X))) TRUE)
((TERM-Y SET) AND
((QQUOTE (E U (U MEMBER-OF TERM-Y))) TRUE)
((QQUOTE (A X1 (X1 MEMBER-OF TERM-Y) (X1 PRED-X)))
TRUE))))
WFF313: (C19 (COLL GUERILLA))
Probability: 1
** INFERRED FORMULA **
WFF322: (C20 GUERILLA)
Rank: 2
Parents: WFF319: (A X (X MEMBER-OF C19) (X GUERILLA))
WFF321: (C20 MEMBER-OF C19)
Probability: 1
Loading story sentence ...
WFF323: (C6 BELONG-TO C19)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF324: (C21 CAR)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF325: (C22 EPISODE)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF326: (C22 DURING C7)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF328: ((C21 EXPLOSIVES-LADEN) ** C22)
Stored in Input Array [1]
Probability: 1
** INFERRED FORMULA **
WFF329: (C21 CARBOMB)
Rank: 1
Parents: WFF22: (A X (X CAR)
(A EP-Y ((X EXPLOSIVES-LADEN) * EP-Y) (X CARBOMB)))
WFF324: (C21 CAR)
WFF328: ((C21 EXPLOSIVES-LADEN) ** C22)
Probability: 1
Loading story sentence ...
WFF330: (C23 EPISODE)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF331: (C23 AFTER-0 C22)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF332: (C23 DURING C7)
Stored in Input Array [1]
Probability: 1
Loading story sentence ...
WFF334: ((C21 BLOW-UP) ** C23)
Stored in Input Array [1]
Probability: 1
** INFERRED FORMULA **
WFF339: (A X ((X NEIGHBORHOOD) AND (C23 OCCURRED-IN X))
(C23 TERRORIST-INCIDENT))
Rank: 1
Parents: WFF120: (A EP-X
(A Y (Y BOMB)
(A Z
((Z NEIGHBORHOOD) AND (EP-X OCCURRED-IN Z)
((Y EXPLODE) * EP-X))
(EP-X TERRORIST-INCIDENT))))
WFF329: (C21 CARBOMB)
WFF334: ((C21 BLOW-UP) ** C23)
Probability: 1
** INFERRED FORMULA **
WFF345: (EP-C24 EPISODE)
Rank: 1
Parents: WFF84: ((E X (X BOMB) (E EP-Y ((X EXPLODE) * EP-Y))) 0.99
(E U ((U EPISODE) AND (EP-Y CAUSE-OF U))
((X DESTROYED) ** U)))
WFF329: (C21 CARBOMB)
WFF334: ((C21 BLOW-UP) ** C23)
Probability: 1
** INFERRED FORMULA **
WFF346: (C23 CAUSE-OF EP-C24)
Rank: 1
Parents: WFF84: ((E X (X BOMB) (E EP-Y ((X EXPLODE) * EP-Y))) 0.99
(E U ((U EPISODE) AND (EP-Y CAUSE-OF U))
((X DESTROYED) ** U)))
WFF329: (C21 CARBOMB)
WFF334: ((C21 BLOW-UP) ** C23)
Probability: 1
** INFERRED FORMULA **
WFF347: (EP-C24 AFTER-0 C23)
Rank: 2
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF345: (EP-C24 EPISODE)
WFF346: (C23 CAUSE-OF EP-C24)
Probability: 1
** INFERRED FORMULA **
WFF348: ((C21 DESTROYED) ** EP-C24)
Rank: 1
Parents: WFF84: ((E X (X BOMB) (E EP-Y ((X EXPLODE) * EP-Y))) 0.99
(E U ((U EPISODE) AND (EP-Y CAUSE-OF U))
((X DESTROYED) ** U)))
WFF329: (C21 CARBOMB)
WFF334: ((C21 BLOW-UP) ** C23)
Probability: 0.99
** INFERRED FORMULA **
WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
Rank: 1
Parents: WFF144: ((E X (X BOMB)
(E Y (Y LOCATION)
(E EP-Z
((EP-Z OCCURRED-NEAR Y) AND
((X EXPLODE) ** EP-Z)))))
0.5
(E U ((U EPISODE) AND (EP-Z CAUSE-OF U))
((Y DAMAGED) ** U)))
WFF329: (C21 CARBOMB)
WFF334: ((C21 BLOW-UP) ** C23)
Probability: 1
** INFERRED FORMULA **
WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
Rank: 1
Parents: WFF73: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
(E EP-X1
((EP-X1 DURING EP-Z) AND (EP-X1 OCCURRED-NEAR X))
(E Y1 (Y1 BOMB) ((Y1 EXPLODE) ** EP-X1))))))
0.9
(E U ((U EPISODE) AND (EP-X1 CAUSE-OF U))
((Y DESTROYED) ** U)))
WFF329: (C21 CARBOMB)
WFF334: ((C21 BLOW-UP) ** C23)
Probability: 1
Loading story sentence ...
WFF363: (C23 OCCURRED-NEAR C6)
Stored in Input Array [1]
Probability: 1
** INFERRED FORMULA **
WFF367: (EP-C25 EPISODE)
Rank: 1
Parents: WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
WFF215: (C6 LOCATION)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF368: (C23 CAUSE-OF EP-C25)
Rank: 1
Parents: WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
WFF215: (C6 LOCATION)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF369: (EP-C25 AFTER-0 C23)
Rank: 2
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF367: (EP-C25 EPISODE)
WFF368: (C23 CAUSE-OF EP-C25)
Probability: 1
** INFERRED FORMULA **
WFF370: ((C6 DAMAGED) ** EP-C25)
Rank: 1
Parents: WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
WFF215: (C6 LOCATION)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 0.5
** INFERRED FORMULA **
WFF374: (EP-C26 EPISODE)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF290: (C16 COMPUTER)
WFF215: (C6 LOCATION)
WFF311: ((C16 IN C6) ** EP-C18)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF375: (C23 CAUSE-OF EP-C26)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF290: (C16 COMPUTER)
WFF215: (C6 LOCATION)
WFF311: ((C16 IN C6) ** EP-C18)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF376: (EP-C26 AFTER-0 C23)
Rank: 2
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF374: (EP-C26 EPISODE)
WFF375: (C23 CAUSE-OF EP-C26)
Probability: 1
** INFERRED FORMULA **
WFF377: ((C16 DESTROYED) ** EP-C26)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF290: (C16 COMPUTER)
WFF215: (C6 LOCATION)
WFF311: ((C16 IN C6) ** EP-C18)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 0.45
** INFERRED FORMULA **
WFF381: (EP-C27 EPISODE)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF275: (C14 DOCUMENT)
WFF215: (C6 LOCATION)
WFF301: ((C14 IN C6) ** EP-C17)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF382: (C23 CAUSE-OF EP-C27)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF275: (C14 DOCUMENT)
WFF215: (C6 LOCATION)
WFF301: ((C14 IN C6) ** EP-C17)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF383: (EP-C27 AFTER-0 C23)
Rank: 2
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF381: (EP-C27 EPISODE)
WFF382: (C23 CAUSE-OF EP-C27)
Probability: 1
** INFERRED FORMULA **
WFF384: ((C14 DESTROYED) ** EP-C27)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF275: (C14 DOCUMENT)
WFF215: (C6 LOCATION)
WFF301: ((C14 IN C6) ** EP-C17)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 0.45
** INFERRED FORMULA **
WFF388: (EP-C28 EPISODE)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF231: (C9 FURNITURE)
WFF215: (C6 LOCATION)
WFF251: ((C9 IN C6) ** EP-C11)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF389: (C23 CAUSE-OF EP-C28)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF231: (C9 FURNITURE)
WFF215: (C6 LOCATION)
WFF251: ((C9 IN C6) ** EP-C11)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF390: (EP-C28 AFTER-0 C23)
Rank: 2
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF388: (EP-C28 EPISODE)
WFF389: (C23 CAUSE-OF EP-C28)
Probability: 1
** INFERRED FORMULA **
WFF391: ((C9 DESTROYED) ** EP-C28)
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF231: (C9 FURNITURE)
WFF215: (C6 LOCATION)
WFF251: ((C9 IN C6) ** EP-C11)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 0.45
** INFERRED FORMULA **
WFF399: ((E X (X PHYSICAL-OBJECT)
(E EP-Y ((X IN C6) ** EP-Y) (C23 DURING EP-Y)))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DESTROYED) ** EP-U)))
Rank: 1
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND (C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF215: (C6 LOCATION)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF400: (C23 OCCURRED-IN C1)
Rank: 1
Parents: WFF32: ((E X (X LOCATION)
(E Y ((Y LOCATION) AND (X IN Y))
(E Z (Z OCCURRED-NEAR X))))
IMPLIES (Z OCCURRED-IN Y))
WFF175: (C1 NEIGHBORHOOD)
WFF215: (C6 LOCATION)
WFF312: (C6 IN C1)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
** INFERRED FORMULA **
WFF338: (C23 TERRORIST-INCIDENT)
Rank: 2
Parents: WFF339: (A X ((X NEIGHBORHOOD) AND (C23 OCCURRED-IN X))
(C23 TERRORIST-INCIDENT))
WFF175: (C1 NEIGHBORHOOD)
WFF400: (C23 OCCURRED-IN C1)
Probability: 1
** INFERRED FORMULA **
WFF404: (EP-C29 EPISODE)
Rank: 2
Parents: WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
WFF175: (C1 NEIGHBORHOOD)
WFF400: (C23 OCCURRED-IN C1)
Probability: 1
** INFERRED FORMULA **
WFF405: (C23 CAUSE-OF EP-C29)
Rank: 2
Parents: WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
WFF175: (C1 NEIGHBORHOOD)
WFF400: (C23 OCCURRED-IN C1)
Probability: 1
** INFERRED FORMULA **
WFF406: (EP-C29 AFTER-0 C23)
Rank: 3
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF404: (EP-C29 EPISODE)
WFF405: (C23 CAUSE-OF EP-C29)
Probability: 1
** INFERRED FORMULA **
WFF407: ((C1 DAMAGED) ** EP-C29)
Rank: 2
Parents: WFF354: ((E X (X LOCATION) (C23 OCCURRED-NEAR X)) 0.5
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DAMAGED) ** EP-U)))
WFF175: (C1 NEIGHBORHOOD)
WFF400: (C23 OCCURRED-IN C1)
Probability: 0.5
** INFERRED FORMULA **
WFF411: (EP-C30 EPISODE)
Rank: 2
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND
(C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF198: (C4 HUMAN)
WFF175: (C1 NEIGHBORHOOD)
WFF210: ((C4 IN C1) ** EP-C5)
WFF400: (C23 OCCURRED-IN C1)
Probability: 1
** INFERRED FORMULA **
WFF412: (C23 CAUSE-OF EP-C30)
Rank: 2
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND
(C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF198: (C4 HUMAN)
WFF175: (C1 NEIGHBORHOOD)
WFF210: ((C4 IN C1) ** EP-C5)
WFF400: (C23 OCCURRED-IN C1)
Probability: 1
** INFERRED FORMULA **
WFF413: (EP-C30 AFTER-0 C23)
Rank: 3
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF330: (C23 EPISODE)
WFF411: (EP-C30 EPISODE)
WFF412: (C23 CAUSE-OF EP-C30)
Probability: 1
** INFERRED FORMULA **
WFF414: ((C4 DESTROYED) ** EP-C30)
Rank: 2
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND
(C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF198: (C4 HUMAN)
WFF175: (C1 NEIGHBORHOOD)
WFF210: ((C4 IN C1) ** EP-C5)
WFF400: (C23 OCCURRED-IN C1)
Probability: 0.9
** INFERRED FORMULA **
WFF421: (EP-C31 EPISODE)
Rank: 3
Parents: WFF95: (A X (X HUMAN)
(A EP-Y ((X DESTROYED) * EP-Y)
(E U
((U EPISODE) AND (U CAUSE-OF EP-Y)
(E V (V THING) ((V KILL X) ** U))))))
WFF198: (C4 HUMAN)
WFF414: ((C4 DESTROYED) ** EP-C30)
Probability: 1
** INFERRED FORMULA **
WFF422: (EP-C31 CAUSE-OF EP-C30)
Rank: 3
Parents: WFF95: (A X (X HUMAN)
(A EP-Y ((X DESTROYED) * EP-Y)
(E U
((U EPISODE) AND (U CAUSE-OF EP-Y)
(E V (V THING) ((V KILL X) ** U))))))
WFF198: (C4 HUMAN)
WFF414: ((C4 DESTROYED) ** EP-C30)
Probability: 0.9
** INFERRED FORMULA **
WFF423: (EP-C30 AFTER-0 EP-C31)
Rank: 4
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF421: (EP-C31 EPISODE)
WFF411: (EP-C30 EPISODE)
WFF422: (EP-C31 CAUSE-OF EP-C30)
Probability: 0.9
** INFERRED FORMULA **
WFF428: (C32 THING)
Rank: 3
Parents: WFF95: (A X (X HUMAN)
(A EP-Y ((X DESTROYED) * EP-Y)
(E U
((U EPISODE) AND (U CAUSE-OF EP-Y)
(E V (V THING) ((V KILL X) ** U))))))
WFF198: (C4 HUMAN)
WFF414: ((C4 DESTROYED) ** EP-C30)
Probability: 1
** INFERRED FORMULA **
WFF430: ((C32 KILL C4) ** EP-C31)
Rank: 3
Parents: WFF95: (A X (X HUMAN)
(A EP-Y ((X DESTROYED) * EP-Y)
(E U
((U EPISODE) AND (U CAUSE-OF EP-Y)
(E V (V THING) ((V KILL X) ** U))))))
WFF198: (C4 HUMAN)
WFF414: ((C4 DESTROYED) ** EP-C30)
Probability: 0.9
** INFERRED FORMULA **
WFF436: (EP-C33 EPISODE)
Rank: 4
Parents: WFF46: (A EP-X
(A Y (Y THING)
(A Z ((Z THING) AND ((Z KILL Y) * EP-X))
(E U ((U EPISODE) AND (EP-X CAUSE-OF U))
((Y DEAD) ** U)))))
WFF428: (C32 THING)
WFF198: (C4 HUMAN)
WFF430: ((C32 KILL C4) ** EP-C31)
Probability: 1
** INFERRED FORMULA **
WFF437: (EP-C31 CAUSE-OF EP-C33)
Rank: 4
Parents: WFF46: (A EP-X
(A Y (Y THING)
(A Z ((Z THING) AND ((Z KILL Y) * EP-X))
(E U ((U EPISODE) AND (EP-X CAUSE-OF U))
((Y DEAD) ** U)))))
WFF428: (C32 THING)
WFF198: (C4 HUMAN)
WFF430: ((C32 KILL C4) ** EP-C31)
Probability: 1
** INFERRED FORMULA **
WFF438: (EP-C33 AFTER-0 EP-C31)
Rank: 5
Parents: WFF53: (A X (X EPISODE)
(A Y ((Y EPISODE) AND (Y CAUSE-OF X))
(X AFTER-0 Y)))
WFF421: (EP-C31 EPISODE)
WFF436: (EP-C33 EPISODE)
WFF437: (EP-C31 CAUSE-OF EP-C33)
Probability: 1
** INFERRED FORMULA **
WFF439: ((C4 DEAD) ** EP-C33)
Rank: 4
Parents: WFF46: (A EP-X
(A Y (Y THING)
(A Z ((Z THING) AND ((Z KILL Y) * EP-X))
(E U ((U EPISODE) AND (EP-X CAUSE-OF U))
((Y DEAD) ** U)))))
WFF428: (C32 THING)
WFF198: (C4 HUMAN)
WFF430: ((C32 KILL C4) ** EP-C31)
Probability: 0.9
** INFERRED FORMULA **
WFF443: ((E X (X PHYSICAL-OBJECT)
(E EP-Y ((X IN C1) ** EP-Y) (C23 DURING EP-Y)))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((X DESTROYED) ** EP-U)))
Rank: 2
Parents: WFF362: ((E X (X LOCATION)
(E Y (Y PHYSICAL-OBJECT)
(E EP-Z ((Y IN X) ** EP-Z)
((C23 DURING EP-Z) AND
(C23 OCCURRED-NEAR X)))))
0.9
(E EP-U ((EP-U EPISODE) AND (C23 CAUSE-OF EP-U))
((Y DESTROYED) ** EP-U)))
WFF175: (C1 NEIGHBORHOOD)
WFF400: (C23 OCCURRED-IN C1)
Probability: 1
** INFERRED FORMULA **
WFF447: ((E X ((X LOCATION) AND (C6 IN X))) IMPLIES (C23 OCCURRED-IN X))
Rank: 1
Parents: WFF32: ((E X (X LOCATION)
(E Y ((Y LOCATION) AND (X IN Y))
(E Z (Z OCCURRED-NEAR X))))
IMPLIES (Z OCCURRED-IN Y))
WFF215: (C6 LOCATION)
WFF363: (C23 OCCURRED-NEAR C6)
Probability: 1
WARNING: Response: Missing translation information for SHIITE-MOSLEM - guessing
A car blew up near an office in a shiite-moslem neighborhood that belonged to a group of guerillas after it was laden with explosives.
The neighborhood was crowded.
There is a group of humans.
Members of the group of humans are in the neighborhood at the same time as the neighborhood was crowded.
There is a member of the group of humans.
Members of the group of humans are humans.
The human is someone.
He was in the neighborhood at the same time as it was crowded.
The office is a location.
The office was full of some furniture at the same time as it was an office.
There is a member of the group of some furniture.
Members of the group of some furniture are furniture.
The furniture is some furniture.
The furniture was in the office at the same time as it was full of the furniture.
Members of the group of some furniture were in the office.
The furniture was in the office.
The office was full of computers and full of documents at the same time as it was an office.
There is a member of the group of documents.
Members of the group of documents are documents.
The document is a document.
There is a member of the group of computers.
Members of the group of computers are computers.
The computer is a computer.
The document was in the office at the same time as it was full of the computers and full of the documents.
Members of the group of documents were in the office.
The document was in the office.
The computer was in the office at the same time as it was full of the computers and full of the documents.
Members of the group of computers were in the office.
The computer was in the office.
There is a member of the group of guerillas.
Members of the group of guerillas are guerillas.
The guerilla is a guerilla.
The car is a carbomb.
If the carbomb blew up in a neighborhood, it is a terrorist incident.
The carbomb was destroyed because it blew up.
The carbomb was destroyed after it blew up.
If the carbomb blew up near a location, it is likely damaged.
If the carbomb blew up near a location while a physical object was in the location, the object is usually destroyed.
The office was damaged because the carbomb blew up.
The office was damaged after the carbomb blew up.
The computer was destroyed because the carbomb blew up.
The computer was destroyed after the carbomb blew up.
The document was destroyed because the carbomb blew up.
The document was destroyed after the carbomb blew up.
The furniture was destroyed because the carbomb blew up.
The furniture was destroyed after the carbomb blew up.
If the carbomb blew up while a physical object was in the office, the object is usually destroyed.
The carbomb blew up.
Blowing up is a terrorist incident.
It was damaged because the carbomb blew up.
It was damaged after the carbomb blew up.
He was destroyed because the carbomb blew up.
He was destroyed after the carbomb blew up.
He was destroyed because he was killed by something.
He was destroyed after he was killed by it.
He was dead because he was killed by it.
He was dead after he was killed by it.
If the carbomb blew up while a physical object was in the neighborhood, the object is usually destroyed.
If the office is in a location, the carbomb blew up in the location.
(WFF174 WFF175 WFF176 WFF177 WFF178 WFF180 WFF190 WFF191 WFF197 WFF195 WFF198
WFF204 WFF208 WFF209 WFF210 WFF211 WFF212 WFF214 WFF215 WFF222 WFF223 WFF230
WFF228 WFF231 WFF237 WFF238 WFF239 WFF246 WFF247 WFF249 WFF251 WFF259 WFF260
WFF266 WFF267 WFF274 WFF272 WFF275 WFF281 WFF282 ...)
EPI(12): T
EPI(13): EPI(13): 0
EPI(14): EPI(14):
Questioning (E U (U BOMB) (E EP-V ((U DESTROYED) * EP-V)))
Answer: YES with probability 0.99
Yes, a carbomb was destroyed.
Used 0.06 seconds CPU time 0.066 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF348 WFF329) (1 PROB33 PROB32 WFF22 PROB37)))
EPI(15): EPI(15):
Questioning (E U (E V ((U OFFICE) * V)) (E EP-W ((U DAMAGED) * EP-W)))
Answer: YES with probability 0.5
Yes, an office was damaged.
Used 0.09 seconds CPU time 0.098 seconds real time
Used 4 iterations Searched to depth 2
((YES (WFF370 WFF214) (1 PROB39 PROB10)))
EPI(16):
Questioning (E U ((E V ((U OFFICE) * V)) AND (E EP-W ((U DAMAGED) * EP-W))))
Answer: YES with probability 0.5
Yes, an office was damaged.
Used 0.13 seconds CPU time 0.126 seconds real time
Used 4 iterations Searched to depth 2
((YES (WFF370 WFF214) (1 PROB39 PROB10)))
EPI(17):
Questioning (E U (U LOCATION)
((E V ((U OFFICE) * V)) AND (E EP-W ((U DAMAGED) * EP-W))))
Answer: YES with probability 0.5
Yes, a location that was an office was damaged.
Used 0.17 seconds CPU time 0.173 seconds real time
Used 7 iterations Searched to depth 2
((YES (WFF214 WFF215 WFF370) (1 PROB10 WFF173 PROB39)))
EPI(18): EPI(18):
Questioning (E U (U FURNITURE) (E EP-V ((U DESTROYED) * EP-V)))
Answer: YES with probability 0.45
Yes, some furniture was destroyed.
Used 0.06 seconds CPU time 0.065 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF391 WFF231) (1 PROB45 PROB13)))
EPI(19): EPI(19):
Questioning (E U (U FURNITURE) (E EP-V ((U BLOW-UP) * EP-V)))
No more actions on agenda
Answer: UNKNOWN
Used 0.17 seconds CPU time 0.171 seconds real time
Used 19 iterations
NIL
EPI(20): EPI(20):
Questioning (E U (U (COLL FURNITURE))
(E V (E W ((V OFFICE) * W)) (E EP-U1 ((V FULL-OF U) ** EP-U1))))
Answer: YES with probability 0.5
Yes, an office was full of some furniture.
Used 0.31 seconds CPU time 0.31 seconds real time
Used 5 iterations Searched to depth 2
((YES (WFF239 WFF222 WFF214) (1 PROB14 WFF222 PROB10)))
EPI(21): EPI(21):
Questioning (E U (U HUMAN) (E EP-V ((U DEAD) * EP-V)))
Answer: YES with probability 0.9
Yes, someone was dead.
Used 0.07 seconds CPU time 0.068 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF439 WFF198) (1 PROB53 PROB5)))
EPI(22):
Questioning (E U (U CAR) (E EP-V ((U DESTROYED) * EP-V)))
Answer: YES with probability 0.99
Yes, a car was destroyed.
Used 0.06 seconds CPU time 0.065 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF348 WFF324) (1 PROB37 PROB32)))
EPI(23):
Questioning (E U (U PHYSICAL-OBJECT) (E V ((U BLOW-UP) * V)))
Answer: YES with probability 1
Yes, a car blew up.
Used 0.18 seconds CPU time 0.18 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF334 WFF324) (1 PROB36 PROB32)))
EPI(24): EPI(24):
Questioning (E U (U PHYSICAL-OBJECT)
(E V (V LOCATION)
(E EP-W (EP-W OCCURRED-NEAR V) ((U EXPLODE) * EP-W))))
Answer: YES with probability 1
Yes, a car blew up in a neighborhood.
Used 0.16 seconds CPU time 0.163 seconds real time
Used 4 iterations Searched to depth 2
((YES (WFF334 WFF324 WFF175 WFF400)
(1 PROB35 PROB9 WFF173 PROB10 PROB2 WFF32 PROB32 PROB36)))
EPI(25):
Questioning (E U (U PHYSICAL-OBJECT)
(E V (V LOCATION)
(E EP-W ((U EXPLODE) * EP-W) (EP-W OCCURRED-NEAR V))))
Answer: YES with probability 1
Yes, a car blew up in a neighborhood.
Used 0.17 seconds CPU time 0.169 seconds real time
Used 4 iterations Searched to depth 2
((YES (WFF334 WFF324 WFF175 WFF400)
(1 PROB35 PROB9 WFF173 PROB10 PROB2 WFF32 PROB32 PROB36)))
EPI(26): EPI(26):
Questioning (E U (U PHYSICAL-OBJECT) (E V ((U IN C1) ** V)))
Answer: YES with probability 1
Yes, someone was in a neighborhood.
Used 0.06 seconds CPU time 0.063 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF210 WFF198) (1 PROB6 PROB5)))
EPI(27):
Questioning (E U (U PHYSICAL-OBJECT) (E V ((U DESTROYED) ** V)))
Answer: YES with probability 0.9
Yes, someone was destroyed.
Used 0.08 seconds CPU time 0.09 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF414 WFF198) (1 PROB49 PROB5)))
EPI(28):
Questioning (E U (U COMPUTER) (E V ((U DESTROYED) ** V)))
Answer: YES with probability 0.45
Yes, a computer was destroyed.
Used 0.06 seconds CPU time 0.061 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF377 WFF290) (1 PROB41 PROB23)))
EPI(29):
Questioning (E U (U (COLL PHYSICAL-OBJECT))
(E V ((C6 FULL-OF U) @ V) (V SAME-TIME C7)))
Answer: YES with probability 0.5
Yes, a location was full of computers at the same time as it was an office.
Used 0.12 seconds CPU time 0.126 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF260 WFF281 WFF303) (1 PROB24 PROB19)))
EPI(30):
Questioning (E U (U (COLL PHYSICAL-OBJECT))
(E V (V SAME-TIME C7) ((C6 FULL-OF U) @ V)))
Answer: YES with probability 0.5
Yes, a location was full of computers at the same time as it was an office.
Used 0.1 seconds CPU time 0.106 seconds real time
Used 2 iterations Searched to depth 1
((YES (WFF260 WFF281 WFF303) (1 PROB24 PROB19)))
EPI(31): EPI(31):
Questioning (E U ((U PHYSICAL-OBJECT) AND (E V ((U IN C6) @ V) (V DURING C7)))
(E W ((U DESTROYED) ** W)))
Answer: YES with probability 0.45
Yes, a computer was in an office while it was an office.
It was destroyed.
Used 0.55 seconds CPU time 0.56 seconds real time
Used 33 iterations Searched to depth 2
((YES (WFF377 WFF290 T-WFF43 WFF311) (1 PROB23 PROB27 T-WFF43 PROB41)))
EPI(32):
Questioning (E U ((U PHYSICAL-OBJECT) AND (E V (V DURING C7) ((U IN C6) @ V)))
(E W ((U DESTROYED) ** W)))
Answer: YES with probability 0.45
Yes, a computer was in an office while it was an office.
It was destroyed.
Used 0.41 seconds CPU time 0.409 seconds real time
Used 33 iterations Searched to depth 2
((YES (WFF377 WFF290 T-WFF42 WFF311) (1 PROB23 PROB27 T-WFF42 PROB41)))
EPI(33):
Questioning (E U
((U PHYSICAL-OBJECT) AND (E V (C23 DURING V) ((U IN C6) @ V)))
(E W ((U DESTROYED) ** W)))
Answer: YES with probability 0.45
Yes, a carbomb blew up while a computer was in a location.
The computer was destroyed.
Used 0.8 seconds CPU time 0.804 seconds real time
Used 33 iterations Searched to depth 2
((YES (WFF377 WFF290 T-WFF42 WFF311) (1 PROB23 PROB27 T-WFF42 PROB41)))
EPI(34):
Questioning (E U (U PHYSICAL-OBJECT)
((E V (V DURING C7) ((U IN C6) @ V)) AND
(E W ((U DESTROYED) ** W))))
Answer: YES with probability 0.45
Yes, a computer was in an office while it was an office.
It was destroyed.
Used 0.44 seconds CPU time 0.442 seconds real time
Used 33 iterations Searched to depth 2
((YES (WFF377 WFF290 T-WFF60 WFF311) (1 PROB23 PROB27 T-WFF60 PROB41)))
EPI(35): EPI(35):
Questioning (A X ((X COMPUTER) AND (E Y (Y DURING C7) ((X IN C6) * Y)))
(A Z (NOT (X DESTROYED) * Z)))
Answer: NO with probability 0.45
No, a computer was in an office while it was an office.
It was destroyed.
Used 0.14 seconds CPU time 0.154 seconds real time
Used 4 iterations Searched to depth 2
((NO (WFF377 WFF290 T-WFF36 WFF311) (1 PROB23 PROB27 T-WFF36 PROB41)))
EPI(36):
Questioning (A X (X COMPUTER)
(A Y ((Y DURING C7) AND ((X IN C6) * Y))
(A Z (NOT (X DESTROYED) * Z))))
Answer: NO with probability 0.45
No, a computer was in an office while it was an office.
It was destroyed.
Used 0.17 seconds CPU time 0.176 seconds real time
Used 4 iterations Searched to depth 2
((NO (WFF377 WFF290 T-WFF53 WFF311) (1 PROB23 PROB27 T-WFF53 PROB41)))
EPI(37):
Questioning ((E X (X COMPUTER) (E Y ((Y DURING C7) AND ((X IN C6) * Y)))) 0.7
(A Z (NOT (X DESTROYED) * Z)))
Answer: NO with probability 0.45
No, a computer was in an office while it was an office.
It was destroyed.
Used 0.16 seconds CPU time 0.156 seconds real time
Used 4 iterations Searched to depth 2
((NO (WFF377 WFF290 T-WFF40 WFF311) (1 PROB23 PROB27 T-WFF40 PROB41)))
EPI(38):
Questioning ((E X (X COMPUTER) (E Y ((Y DURING C7) AND ((X IN C6) * Y)))) 0.4
(A Z (NOT (X DESTROYED) * Z)))
Answer: NO with probability 0.45
No, a computer was in an office while it was an office.
It was destroyed.
Used 0.22 seconds CPU time 0.229 seconds real time
Used 4 iterations Searched to depth 2
((NO (WFF377 WFF290 T-WFF40 WFF311) (1 PROB23 PROB27 T-WFF40 PROB41)))
EPI(39):
Questioning ((E X (X COMPUTER) (E Y ((Y DURING C7) AND ((X IN C6) * Y))))
IMPLIES (A Z (NOT (X DESTROYED) * Z)))
Answer: NO with probability 0.45
No, a computer was in an office while it was an office.
It was destroyed.
Used 0.16 seconds CPU time 0.158 seconds real time
Used 4 iterations Searched to depth 2
((NO (WFF377 WFF290 T-WFF40 WFF311) (1 PROB23 PROB27 T-WFF40 PROB41)))
EPI(40): EPI(40): T
EPI(41):
Questioning (A X
((X PHYSICAL-OBJECT) AND
(E Y ((Y EPISODE) AND (Y DURING C7)) ((X IN C6) ** Y)))
(E U ((X DESTROYED) ** U)))
Maximum number of iterations (40) reached. Use (q) to continue.
Answer: UNKNOWN
Used 1.6 seconds CPU time 1.605 seconds real time
Used 40 iterations
NIL
EPI(42): T
EPI(43):
Questioning (A X
((X PHYSICAL-OBJECT) AND
(E Y ((Y EPISODE) AND (C23 DURING Y)) ((X IN C6) ** Y)))
(E U ((X DESTROYED) ** U)))
Answer: YES with probability 0.9
Yes, if a carbomb blew up while a physical object was in a location, the object is usually destroyed.
Used 0.31 seconds CPU time 0.313 seconds real time
Used 15 iterations Searched to depth 3
((YES (WFF399) (0.9 PROB33 PROB32 WFF22 PROB36 WFF73 PROB35 WFF173 PROB10)))
EPI(44): T
EPI(45): T
EPI(46):
Questioning (A X
((X PHYSICAL-OBJECT) AND
(E Y ((Y EPISODE) AND (Y DURING C7)) ((X IN C6) ** Y)))
(E U ((X DESTROYED) ** U)))
Maximum number of iterations (40) reached. Use (q) to continue.
Answer: UNKNOWN
Used 2.59 seconds CPU time 2.585 seconds real time
Used 40 iterations
NIL
EPI(47): T
EPI(48):
Questioning (A X
((X PHYSICAL-OBJECT) AND
(E Y ((Y EPISODE) AND (C23 DURING Y)) ((X IN C6) ** Y)))
(E U ((X DESTROYED) ** U)))
Answer: YES with probability 0.9
Yes, if a carbomb blew up while a physical object was in a location, the object is usually destroyed.
Used 0.7 seconds CPU time 0.7 seconds real time
Used 9 iterations Searched to depth 2
((YES (WFF399) (0.9 PROB33 PROB32 WFF22 PROB36 WFF73 PROB35 WFF173 PROB10)))
EPI(49): NIL
EPI(50):
Questioning (A X (X COMPUTER) (E U (U EPISODE) ((X DESTROYED) ** U)))
Maximum number of iterations (40) reached. Use (q) to continue.
Answer: UNKNOWN
Used 0.34 seconds CPU time 0.334 seconds real time
Used 40 iterations
NIL
EPI(51): EPI(51):
Questioning (WH WH-U (E V ((WH-U DESTROYED) @ V)))
Stopping after 1 answers
Answer(s):
(C4) with probability 0.9
Someone was destroyed.
Used 0.2 seconds CPU time 0.203 seconds real time
Used 2 iterations Searched to depth 1
(((C4) (WFF414) (1 PROB49)))
EPI(52):
Questioning (WH WH-U (E V ((WH-U DESTROYED) @ V)))
Answer(s):
(C9) with probability 0.45
(C14) with probability 0.45
(C16) with probability 0.45
(C21) with probability 0.99
(C4) with probability 0.9
Some furniture was destroyed.
A document was destroyed.
A computer was destroyed.
A carbomb was destroyed.
Someone was destroyed.
Used 0.28 seconds CPU time 0.295 seconds real time
Used 22 iterations Searched to depth 1
(((C9) (WFF391) (1 PROB45)) ((C14) (WFF384) (1 PROB43))
((C16) (WFF377) (1 PROB41)) ((C21) (WFF348) (1 PROB37))
((C4) (WFF414) (1 PROB49)))
EPI(53):
Questioning (WH WH-U (E V ((WH-U DAMAGED) @ V)))
Depth much lower than previous answers
Answer(s):
(C6) with probability 0.5
(C1) with probability 0.5
A location was damaged.
A neighborhood was damaged.
Used 0.25 seconds CPU time 0.274 seconds real time
Used 18 iterations Searched to depth 1
(((C6) (WFF370) (1 PROB39)) ((C1) (WFF407) (1 PROB47)))
EPI(54): 3
EPI(55):
Questioning (WH WH-U (E V ((WH-U DAMAGED) @ V)))
Answer(s):
(BEIRUT) with probability 0.5
(C6) with probability 0.5
(C1) with probability 0.5
A carbomb blew up in a neighborhood in the city.
If something is near a location in another location, it is in that other location.
If the carbomb blew up near a location, it is likely damaged.
A location was damaged.
The neighborhood was damaged.
Used 0.4 seconds CPU time 0.413 seconds real time
Used 40 iterations Searched to depth 4
(((BEIRUT) (WFF400 WFF175 WFF174 WFF177 WFF32 WFF354)
(0.5 PROB35 PROB9 WFF173 PROB10 PROB2 WFF32 WFF174 WFF144 PROB36 WFF22
PROB32 PROB33))
((C6) (WFF370) (1 PROB39)) ((C1) (WFF407) (1 PROB47)))
EPI(56): 0
EPI(57):
Questioning (WH WH-U (E V ((WH-U EXPLODE) @ V)))
Depth much lower than previous answers
Answer(s):
(C21) with probability 1
A carbomb blew up.
Used 0.23 seconds CPU time 0.233 seconds real time
Used 14 iterations Searched to depth 1
(((C21) (WFF334) (1 PROB36)))
EPI(58):
Questioning (WH WH-U (WH WH-V (E W ((WH-U KILL WH-V) @ W))))
Depth much lower than previous answers
Answer(s):
(C32 C4) with probability 0.9
Something killed someone.
Used 0.53 seconds CPU time 0.536 seconds real time
Used 27 iterations Searched to depth 1
(((C32 C4) (WFF430) (1 PROB52)))
EPI(59):
Questioning (WH WH-U (E V ((C6 FULL-OF WH-U) @ V)))
Depth much lower than previous answers
Answer(s):
(C8) with probability 0.5
(C15) with probability 0.5
A location was full of a set.
It was full of a set.
Used 0.26 seconds CPU time 0.253 seconds real time
Used 20 iterations Searched to depth 1
(((C8) (WFF239) (1 PROB14)) ((C15) (WFF303) (1 PROB24)))
EPI(60):
Questioning (WH WH-U (WH WH-V (E W ((WH-U IN WH-V) @ W))))
Depth much lower than previous answers
Answer(s):
(C9 C6) with probability 0.5
(C14 C6) with probability 0.5
(C16 C6) with probability 0.5
(C4 C1) with probability 1
Some furniture was in a location.
A document was in the location.
A computer was in the location.
Someone was in a neighborhood.
Used 0.54 seconds CPU time 0.541 seconds real time
Used 38 iterations Searched to depth 1
(((C9 C6) (WFF251) (1 PROB13 PROB16)) ((C14 C6) (WFF301) (1 PROB21 PROB25))
((C16 C6) (WFF311) (1 PROB23 PROB27)) ((C4 C1) (WFF210) (1 PROB6)))
EPI(61):
Questioning (WH WH-U (E V ((WH-U DEAD) @ V)))
Depth much lower than previous answers
Answer(s):
(C4) with probability 0.9
Someone was dead.
Used 0.23 seconds CPU time 0.234 seconds real time
Used 15 iterations Searched to depth 1
(((C4) (WFF439) (1 PROB53)))
EPI(62): EPI(62): 40
EPI(63): NIL
EPI(64): EOF
Really exit lisp [n]?
; Exiting Lisp