s e l s b u s r e w f n o l i s c k o m l a r x -b t g e d...
TRANSCRIPT
8/13
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
Stan
dard
izin
g X
ML
Rul
es:
Rul
es fo
r E
-Bus
ines
son
the
Sem
anti
c W
eb(I
nvite
d T
alk)
Ben
jam
in G
roso
f
MIT
Slo
an S
choo
l of
Man
agem
ent,
Info
rmat
ion
Tec
hnol
ogy
grou
p
bgro
sof@
mit.
edu
h
ttp://
ww
w.m
it.ed
u/~b
gros
of/
Slid
es p
rese
nted
at I
JCA
I-01
Wor
ksho
p on
E-b
usin
ess
and
the
Inte
llig
ent W
eb, A
ug. 5
, 200
1ht
tp:/
/ww
w.ij
cai-
01.o
rg ;
htt
p://
ww
w.c
sd.a
bdn.
ac.u
k/eb
iweb
/
8/13
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
Out
line
of T
alk
•In
trod
uctio
n: B
ackg
roun
d, M
otiv
atio
n
•Fu
ndam
enta
l Tec
hnic
al I
ssue
s an
d A
ppro
ache
s
–he
tero
gene
ous
com
mer
cial
rul
e sy
stem
s/re
p’ns
–ev
olut
iona
ry s
trat
egy
for
stan
dard
s
–lo
gic
prog
ram
s an
d ex
tens
ions
•L
ates
t ite
ratio
n: R
uleM
L
–W
ebiz
ing
•N
ext S
teps
8/13
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Ben
jam
in G
roso
f M
IT
copy
right
s re
serv
ed
Impo
rtan
t KR
’s to
day
in E
-Bus
ines
s
•R
ules
, rel
atio
nal d
atab
ases
–em
ergi
ng s
tand
ard:
Rul
eML
•D
escr
iptio
n L
ogic
, fra
mes
, tax
onom
ies
–em
ergi
ng s
tand
ard:
DA
ML
+O
IL
•(o
ther
) C
lass
ical
Log
ic–
emer
ging
sta
ndar
d: K
now
ledg
e In
terc
hang
e Fo
rmat
(K
IF)
•B
ayes
Net
s &
Dec
isio
n T
heor
y: p
roba
bilit
ies,
dep
ende
ncie
s, u
tiliti
es
–ea
rly,
pri
mar
ily f
or r
esea
rche
rs:
Bay
es N
et I
nter
chan
ge F
orm
at (
BN
IF)
•(o
ther
) D
ata
Min
ing
indu
ctiv
e pr
edic
tive
mod
els:
neu
ral n
ets,
asso
ciat
ions
, fuz
zy, r
egre
ssio
ns, …
--
ear
ly:
Pred
ictiv
e M
odel
Mar
kup
Lan
g.
•A
rgua
bly:
Sem
i-St
ruct
ured
Dat
a: X
ML
Que
ry, R
DF
•A
rgua
bly:
UM
L
8/13
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Ben
jam
in G
roso
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IT
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s re
serv
ed
App
lica
tion
s of
Age
nt C
omm
unic
atio
n in
Kno
wle
dge-
Bas
ed E
-Mar
kets
(K
BE
M)
•B
ids
in
auc
tions
and
rev
erse
auc
tions
•O
rder
s i
n su
pply
cha
in o
r B
2C
•C
ontr
acts
/Dea
ls/P
ropo
sals
/Req
uest
sFor
Prop
osal
s
–pr
ices
; pr
oduc
t/ser
vice
des
crip
tions
; re
fund
s, c
ontin
genc
ies
•B
uyer
/Sel
ler
inte
rest
s, p
refe
renc
es, c
apab
ilitie
s, p
rofi
les
–re
com
men
der
syst
ems;
yel
low
pag
es; c
atal
ogs
•R
atin
gs, r
eput
atio
ns; c
usto
mer
fee
dbac
k or
pro
blem
s•
Dem
and
fore
cast
s i
n m
anuf
actu
ring
sup
ply
chai
n
•C
onst
rain
ts
in tr
avel
pla
nnin
g
•C
redi
twor
thin
ess,
trus
twor
thin
ess,
3rd
-par
ty r
ecom
men
datio
ns
•In
dust
ry-v
ertic
als:
com
pute
r pa
rts,
rea
l est
ate,
…
8/13
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Ben
jam
in G
roso
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IT
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s re
serv
ed
Tec
hnol
ogy
Res
earc
h D
irec
tion
s:K
R fo
r A
gent
Com
mun
icat
ion
•A
ims:
–de
eper
rea
soni
ng in
tra-
agen
t
•“u
nder
stan
ding
” w
hat r
ecei
ve
–m
ore
mod
ular
ity in
:
•co
nten
t
•so
ftw
are
engi
neer
ing
–K
R o
f th
e ki
nd n
eede
d fo
r e-
mar
ket a
pplic
atio
ns
•ca
talo
gs, c
ontr
acts
, neg
otia
tion/
auct
ions
, tru
st,
prof
iles/
pref
eren
ces/
targ
etin
g, …
–pl
ay w
ith X
ML
sta
ndar
ds, c
apab
ilitie
s, m
enta
lity
8/13
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
Tec
hnol
ogy
Res
earc
h D
irec
tion
:K
R o
n th
e W
eb•
App
ly K
R v
iew
poin
t and
tech
niqu
es to
Web
info
•“W
eb-i
ze”
the
KR
’s
–ex
ploi
t Web
/XM
L h
yper
-lin
ks, i
nter
face
s, to
ols
–th
ink
glob
al, a
ct g
loba
l :
as
part
of
who
le W
eb
•R
adic
ally
rai
se th
e le
vel o
f sh
ared
mea
ning
–le
vel =
con
cept
ual/a
bstr
actio
n le
vel
–m
eani
ng =
san
ctio
ned
infe
renc
es /
voca
bula
ries
–sh
ared
= ti
ght c
orre
spon
denc
e
•“T
he S
eman
tic W
eb”,
“T
he W
eb o
f T
rust
” [T
im B
-L]
•B
uild
: T
he W
eb M
ark
II
8/13
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
Why
Sta
ndar
dize
Rul
es N
ow?
•R
ules
as
a fo
rm o
f K
R (
know
ledg
e re
pres
enta
tion)
are
espe
cial
ly u
sefu
l:
–re
lativ
ely
mat
ure
from
bas
ic r
esea
rch
view
poin
t
–go
od f
or p
resc
ript
ive
spec
ific
atio
ns (v
s. d
escr
iptiv
e)
•a
rest
rict
ed p
rogr
amm
ing
mec
hani
sm
–in
tegr
ate
wel
l int
o co
mm
erci
ally
mai
nstr
eam
soft
war
e en
gine
erin
g, e
.g.,
OO
and
DB
•ea
sily
em
bedd
able
; fam
iliar
•ve
ndor
s in
tere
sted
alr
eady
: W
ebiz
ing,
app
. dev
. too
ls
•⇒
⇒ I
dent
ifie
d as
par
t of m
issi
on o
f the
W3C
Sem
anti
cW
eb A
ctiv
ity
8/13
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Ben
jam
in G
roso
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IT
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s re
serv
ed
Vis
ion:
Use
s of
Rul
es in
E-B
usin
ess
•R
ules
as
an im
port
ant a
spec
t of
com
ing
wor
ld o
f In
tern
et e
-bus
ines
s:ru
le-b
ased
bus
ines
s po
licie
s &
bus
ines
s pr
oces
ses,
for
B2B
& B
2C.
–re
pres
ent s
elle
r’s
offe
ring
s of
pro
duct
s &
ser
vice
s, c
apab
ilitie
s, b
ids;
map
off
erin
gs f
rom
mul
tiple
sup
plie
rs to
com
mon
cat
alog
.
–re
pres
ent b
uyer
’s r
eque
sts,
inte
rest
s, b
ids;
→
mat
chm
akin
g.
–re
pres
ent s
ales
hel
p, c
usto
mer
hel
p, p
rocu
rem
ent,
auth
oriz
atio
n/tr
ust,
brok
erin
g, w
orkf
low
.
–hi
gh le
vel o
f co
ncep
tual
abs
trac
tion;
eas
ier
for
non-
prog
ram
mer
s to
unde
rsta
nd, s
peci
fy, d
ynam
ical
ly m
odif
y &
mer
ge.
–ex
ecut
able
but
can
trea
t as
data
, sep
arat
e fr
om c
ode
•po
tent
ially
ubi
quito
us; a
lrea
dy w
ide:
e.g
., SQ
L v
iew
s, q
ueri
es.
•R
ules
in c
omm
unic
atin
g ap
plic
atio
ns, e
.g.,
embe
dded
inte
llige
nt a
gent
s.
8/13
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
•E
.g.,
in O
O a
pp’s
, DB
’s, w
orkf
low
s.
•R
elat
iona
l dat
abas
es, S
QL
: V
iew
s, q
ueri
es, f
acts
are
all
rule
s.
•Pr
oduc
tion
rule
s (O
PS5
heri
tage
): e
.g.,
–B
laze
, IL
OG
, Hal
ey:
rul
e-ba
sed
Java
/C+
+ o
bjec
ts.
•E
vent
-Con
ditio
n-A
ctio
n ru
les
(loo
se f
amily
), c
f.:
–bu
sine
ss p
roce
ss a
utom
atio
n / w
orkf
low
tool
s.
–ac
tive
data
base
s; p
ublis
h-su
bscr
ibe.
•Pr
olog
. “
logi
c pr
ogra
ms”
as
a fu
ll p
rogr
amm
ing
lang
uage
.
•(L
esse
r: o
ther
kno
wle
dge-
base
d sy
stem
s.)
Fla
vors
of R
ules
Com
mer
cial
ly M
ost
Impo
rtan
t tod
ay in
E-B
usin
ess
8/13
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Ben
jam
in G
roso
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IT
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s re
serv
ed
Stan
dard
izin
g X
ML
Rul
es:
Ove
rall
Goa
lsz
Prov
ide
a ba
sis
for
a st
anda
rdiz
ed r
ule
mar
kup
lang
uage
,w
ith d
ecla
rativ
e K
R s
eman
tics
zin
tero
pera
bilit
y of
het
erog
eneo
us r
ule
syst
ems
and
appl
icat
ions
zin
form
atio
n in
tegr
atio
n of
het
erog
eneo
us r
ule
KB
’s/s
ervi
ces
zSt
art w
ith c
omm
erci
ally
impo
rtan
t fla
vors
of
rule
s
zSt
art s
impl
e w
ith a
ker
nel K
R, t
hen
add
exte
nsio
nsin
crem
enta
lly.
8/13
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
Stan
dard
izin
g X
ML
Rul
es:
Mor
e G
oals
zA
dd e
xten
sion
s in
crem
enta
lly to
:
zra
ise
KR
exp
ress
iven
ess
and
synt
actic
con
veni
ence
zco
nnec
t cle
anly
to p
roce
dura
l mec
hani
sms
zpa
ss-t
hru/
bund
le-i
n sy
stem
-spe
cifi
c (m
eta-
)inf
o
zex
ploi
t Web
-wor
ld f
unct
iona
lity,
sta
ndar
ds
zSy
nerg
ize
with
oth
er K
R a
spec
ts o
f S
eman
tic W
eb:
zR
DF
; O
ntol
ogie
s: D
AM
L+
OIL
/Des
crip
tion-
Log
ic
zru
les
in/f
or o
ntol
ogie
s, o
ntol
ogie
s fo
r/of
rul
es
zC
ompl
emen
t XM
L n
on-S
W o
ntol
ogie
s al
read
y ev
olvi
ng
zSy
nerg
ize
with
oth
er W
eb s
tand
ards
: P
3P A
PPE
L, X
ML
Que
ry,
Web
Ser
vice
s, ..
.
8/13
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
•In
itia
l Ste
p: K
eep
It S
impl
e, f
ocus
pri
mar
ily o
n:
–C
urre
ntly
Com
mer
cial
ly I
mpo
rtan
t (C
CI)
kin
ds o
f ru
les
–w
ith X
ML
syn
tax
–w
ith s
hare
d se
man
tics
and
inte
rope
rabi
lity
–B
UT
: fo
rese
e to
max
. sm
ooth
evo
lutio
n, b
ack-
com
pati
bilit
y
•L
ater
: ge
t fan
cier
in r
egar
d to
:
–W
eb-i
zing
: fe
atur
es, s
yner
gy w
ith o
ther
sta
ndar
ds
–K
R e
xpre
ssiv
enes
s
–in
corp
orat
e ne
w f
unda
men
tal r
esea
rch
resu
lts &
con
sens
us
•R
atio
nale
: s
peed
acc
epta
nce
& d
eplo
ymen
t; a
void
“bl
eedi
ng e
dge”
Incr
emen
tal S
trat
egy
ofSt
anda
rds
Dev
elop
men
t
8/13
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
•A
naly
tic
Insi
ght [
man
y]:
–H
orn
FOL
is a
sha
red
KR
sem
. E
.g.,
KIF
con
form
ance
leve
l
•A
naly
tic
Insi
ght [
Gro
sof 9
9]:
–!!
Can
do
bett
er -
- c
lose
r, m
ore
expr
essi
ve!!
–St
art w
ith H
orn
Log
ic P
rogr
am (
LP)
, esp
. Dat
alog
•cl
oser
cor
resp
onde
nce
to w
hat C
CI
rule
sys
tem
s ac
tual
ly d
o
•ge
nera
te g
roun
d-lit
eral
con
clus
ions
onl
y, n
o ot
her
“tau
tolo
gies
” (e
.g.,
OR
’s)
•U
niqu
e N
ames
Ass
umpt
ion
(UN
A)
is ty
pica
l; o
pt.:
exp
licitl
y ad
d eq
ualit
ies
•{D
atal
og +
{bo
unde
d #
logi
cal v
aria
bles
per
rul
e} }
is
fre
quen
t, tr
acta
ble
–E
xten
d L
P to
neg
atio
n, p
rior
ities
, pro
cedu
res
• n
eede
d in
CC
I ru
le s
yste
ms,
fai
rly
wel
l-un
ders
tood
fun
dam
enta
lly
Tec
hnic
al C
hall
enge
#1:
whi
ch in
itia
l cor
e K
R s
eman
tics
?
8/13
/200
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Ben
jam
in G
roso
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IT
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right
s re
serv
ed
•C
CI
non-
mon
oton
icity
is h
eavi
ly u
sed,
incl
udes
:
–ne
gatio
n
–pr
iori
ties
(Pro
log,
OPS
5, D
B u
pdat
es, i
nher
itanc
e ex
cept
ions
)
•C
omm
on C
CI
The
me:
ena
ble
mod
ular
ity in
spe
cifi
catio
n
•A
naly
tic
Insi
ght [
man
y]:
–ne
gatio
n-as
-fai
lure
(N
AF
), n
ot c
lass
ical
neg
atio
n, is
the
form
of
nega
tion
typi
cally
use
d in
CC
I
•m
ore
natu
ral/e
asy
to im
plem
ent,
mor
e fl
exib
le
Tec
hnic
al C
hall
enge
#2:
how
to h
andl
e C
CI
non-
mon
oton
icit
y?
8/13
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Ben
jam
in G
roso
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IT
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s re
serv
ed
•ca
noni
cal s
eman
tics
of N
AF
in L
P is
wel
l-un
ders
tood
theo
retic
ally
sinc
e 19
90’s
:–
Wel
l-Fo
unde
d Se
man
tics
(WFS
); n
uanc
ed f
or u
nres
tric
tedl
y re
curs
ive
rule
s
–co
nsen
sus
has
form
ed in
fun
dam
enta
l res
earc
h co
mm
unity
–on
ly m
odes
tly in
crea
ses
com
puta
tiona
l com
plex
ity c
ompa
red
to H
orn
(fre
quen
tly li
near
, at w
orst
qua
drat
ic)
•...
but p
ract
ice
in P
rolo
g an
d ot
her
CC
I is
oft
en “
slop
py”
(inc
ompl
ete
/ cut
-cor
ners
) re
lativ
e to
can
onic
al s
eman
tics
–in
cas
es o
f re
curs
ive
rule
s, W
FS a
lgor
ithm
s re
quir
ed a
re m
ore
com
plex
–on
goin
g di
ffus
ion
of W
FS th
eory
& a
lgor
ithm
s, b
egin
ning
in P
rolo
g’s
Sem
anti
cs o
f Neg
atio
n A
s F
ailu
re in
CC
I
8/13
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Ben
jam
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serv
ed
•{H
orn
LP
} +
NA
F =
“O
rdin
ary”
LP
(O
LP
)
–a.
k.a.
“ge
nera
l”, “
norm
al”,
…
–e.
g., “
pure
” Pr
olog
is b
ackw
ard-
dire
ctio
n O
LP
Ord
inar
y L
ogic
Pro
gram
s as
Sha
red
KR
8/13
/200
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
•Sy
nthe
tic
Insi
ght [
Gro
sof
97..9
9]:
–“C
ourt
eous
” L
P (C
LP)
[G
roso
f 9
7..9
9] is
abl
e to
repr
esen
t the
bas
ic k
inds
of
prio
ritie
s us
ed in
CC
I•
stat
ic r
ule
sequ
ence
, e.g
., in
Pro
log
•dy
nam
ical
ly-c
ompu
ted
rule
seq
uenc
e, e
.g.,
in O
PS5
•in
heri
tanc
e w
ith e
xcep
tions
•D
B u
pdat
es
–C
LP
onl
y m
oder
atel
y in
crea
ses
com
puta
tiona
l com
plex
ityco
mpa
red
to O
LP
(fre
quen
tly li
near
, wor
st-c
ase
cubi
c)
–C
LP
mod
ular
for
sof
twar
e en
gine
erin
g
•co
mpi
leab
le in
to O
LP
(pre
serv
ing
onto
logy
)
how
to h
andl
e C
CI
non-
mon
oton
icit
y?
cont
inue
d
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
EE
CO
MS
Exa
mpl
e of
Con
flic
ting
Rul
es:
Ord
erin
g L
ead
Tim
e
•V
endo
r’s
rule
s th
at p
resc
ribe
how
buy
er m
ust p
lace
or
mod
ify
an o
rder
:
•A
) 14
day
s ah
ead
if th
e bu
yer
is a
qua
lifie
d cu
stom
er.
•B
) 30
day
s ah
ead
if th
e or
dere
d ite
m is
a m
inor
par
t.
•C
) 2
days
ahe
ad if
the
orde
red
item
’s it
em-t
ype
is b
ackl
ogge
d at
the
vend
or,
the
orde
r is
a m
odif
icat
ion
to r
educ
e th
e qu
antit
y of
the
item
, and
the
buye
r is
aqu
alif
ied
cust
omer
.
•Su
ppos
e m
ore
than
one
of
the
abov
e ap
plie
s to
the
curr
ent o
rder
? C
onfl
ict!
•H
elpf
ul A
ppro
ach:
pre
cede
nce
betw
een
the
rule
s. O
ften
onl
y pa
rtia
l ord
er o
fpr
eced
ence
is ju
stif
ied.
E.g
., C
> A
.
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
Cou
rteo
us L
P’s
:O
rder
ing
Lea
d T
ime
Exa
mpl
e•
<le
adT
imeR
ule1
> o
rder
Mod
ific
atio
nNot
ice(
?Ord
er,1
4day
s)
•
←
pr
efer
redC
usto
mer
Of(
?Buy
er,?
Sell
er)
∧•
p
urch
aseO
rder
(?O
rder
,?B
uyer
,?Se
ller
) .
•<
lead
Tim
eRul
e2>
ord
erM
odif
icat
ionN
otic
e(?O
rder
,30d
ays)
•
←
min
orPa
rt(?
Buy
er,?
Sell
er,?
Ord
er)
∧•
pur
chas
eOrd
er(?
Ord
er,?
Buy
er,?
Sell
er)
.
•<
lead
Tim
eRul
e3>
ord
erM
odif
icat
ionN
otic
e(?O
rder
,2da
ys)
•
←
pre
ferr
edC
usto
mer
Of(
?Buy
er,?
Sell
er)
∧•
or
derM
odif
icat
ionT
ype(
?Ord
er,r
educ
e) ∧
•
orde
rIte
mIs
InB
ackl
og(?
Ord
er)
∧•
pur
chas
eOrd
er(?
Ord
er,?
Buy
er,?
Sell
er)
.
•ov
erri
des(
lead
Tim
eRul
e3 ,
lead
Tim
eRul
e1)
.
•⊥
← o
rder
Mod
ific
atio
nNot
ice(
?Ord
er,?
X)
∧•
ord
erM
odif
icat
ionN
otic
e(?O
rder
,?Y
); G
IVE
N ?
X ≠
?Y.
8/13
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
•
Igno
ring
pro
cedu
ral c
ontr
ol (c
f. in
fere
ncin
g co
ntro
l str
ateg
ies)
…
•C
CI
proc
edur
al a
spec
ts a
re h
eavi
ly u
sed,
incl
udin
g:–
Prol
og:
built
-ins
–O
PS5/
EC
A:
actio
ns, s
ome
cond
ition
s
• k
ey to
em
bedd
abili
ty in
mai
nstr
eam
sof
twar
e de
v.
–“t
rigg
ers”
and
“ac
tive
rule
s” in
rel
atio
nal D
B’s
•A
naly
tic
Insi
ght [
Gro
sof
99]:
– v
iew
as
proc
edur
al a
ttach
men
ts (
cf. K
R th
eory
)
Tec
hnic
al C
hall
enge
#3:
how
to h
andl
e C
CI
proc
edur
al a
spec
ts?
8/13
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Ben
jam
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roso
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IT
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right
s re
serv
ed
•Sy
nthe
tic
Insi
ght [
Gro
sof
95..0
0]:
–“S
ituat
ed”
LP
(SL
P)
[Gro
sof
97.
.00]
app
ears
abl
e to
repr
esen
t the
bas
ic k
inds
of
proc
edur
al a
ttach
men
tsus
ed in
CC
I, th
ough
with
mor
e di
scip
line(
/res
tric
tions
)•
“apr
oc”
= e
xter
nal a
ttach
ed p
roce
dure
•“e
ffec
ting”
: dra
win
g pu
re-b
elie
f co
nclu
sion
trig
gers
invo
catio
n of
act
ion
apro
c fo
r sa
ke o
f its
sid
e-ef
fect
s
•“s
ensi
ng”:
tes
t pur
e-be
lief
ante
cede
nt c
ondi
tion
by in
voki
ngpu
rely
-inf
orm
atio
nal q
uery
to a
proc
•di
scip
line:
res
tric
t sta
te c
hang
es f
rom
ext
erna
l pro
cedu
res
–qu
eryi
ng (
sens
or)
atta
ched
pro
cedu
res
does
not
cha
nge
stat
e
–pe
rfor
min
g ef
fect
or a
ssoc
iate
pre
dica
tes
with
ext
erna
l pro
cedu
res
how
to h
andl
e C
CI
proc
edur
al a
spec
ts?
cont
inue
d
8/13
/200
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
Sit
uate
d L
P’s
: O
verv
iew
• p
hone
Num
berO
fPre
dica
te
::s::
B
oein
gBlu
ePag
esC
lass
.get
Phon
eMet
hod
.ex
. Of s
enso
r st
atem
ent
• s
houl
dSen
dPag
ePre
dica
te
::e::
AT
TPa
gerC
lass
.goP
ageM
etho
d .
ex. e
ffec
tor
stat
emen
t
•Se
nsor
pro
cedu
re m
ay r
equi
re s
ome
argu
men
ts to
be
grou
nd, i
.e.,
boun
d; in
gen
eral
it h
as a
spe
cifi
ed b
indi
ng-s
igna
ture
.
•E
nabl
e dy
nam
ic lo
adin
g an
d re
mot
e lo
adin
g of
the
atta
ched
pro
cedu
res
(exp
loit
Java
goo
dnes
s).
•O
vera
ll: c
lean
ly s
epar
ate
out t
he p
roce
dura
l sem
antic
s as
a d
ecla
rativ
eex
tens
ion
of th
e pu
re-b
elie
f de
clar
ativ
e se
man
tics.
Eas
ily s
epar
ate
chai
ning
fro
m a
ctio
n.
8/13
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Ben
jam
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roso
f M
IT
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right
s re
serv
ed
Goi
ng B
eyon
d K
IF
•K
IF is
KR
Ag.
Com
m. L
ang.
’s p
oint
of
depa
rtur
e:
–In
tent
: ge
nera
l-kn
owle
dge
inte
rlin
gua.
–E
mer
ging
sta
ndar
d, in
AN
SI
com
mm
ittee
.
–M
ain
focu
s: c
lass
ical
logi
c, e
sp. f
irst
-ord
er.
•T
his
is th
e de
clar
ativ
e co
re, w
ith d
eep
sem
antic
s.
–H
as m
ajor
lim
itatio
ns:
•ge
nera
l-pu
rpos
e-ne
ss
•lo
gica
lly m
onot
onic
•pu
re-b
elie
f–
no in
voki
ng o
f pr
oced
ures
ext
erna
l to
the
infe
renc
e en
gine
.
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
Cri
teri
a fo
r A
gent
-Com
mun
icat
ion
Rul
e R
epre
sent
atio
n
•H
igh-
leve
l: A
gent
s re
ach
com
mon
und
erst
andi
ng; r
ules
et is
eas
ilym
odif
iabl
e, c
omm
unic
atab
le, e
xecu
tabl
e.
•In
ter-
oper
ate:
het
erog
eneo
us c
omm
erci
ally
impo
rtan
t rul
e sy
stem
s.
•E
xpre
ssiv
e po
wer
, con
veni
ence
, nat
ural
-nes
s.
•...
but
: co
mpu
tatio
nal t
ract
abili
ty.
•M
odul
arity
and
loca
lity
in r
evis
ion.
•D
ecla
rativ
e se
man
tics.
•L
ogic
al n
on-m
onot
onic
ity:
defa
ult r
ules
, neg
atio
n-as
-fai
lure
.
–es
sent
ial f
eatu
re in
com
mer
cial
ly im
port
ant r
ule
syst
ems.
•Pr
iori
tized
con
flic
t han
dlin
g.
•E
ase
of p
arsi
ng.
•In
tegr
atio
n in
to W
eb-w
orld
sof
twar
e en
gine
erin
g.
•Pr
oced
ural
atta
chm
ents
.
1 2 3
OL
P} C
ourt
eous
}X
ML
Situ
ated
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
IBM
’s B
usin
ess
Rul
es M
arku
p L
angu
age
(BR
ML
) an
d C
omm
onR
ules
•T
he a
bove
app
roac
h w
ith S
CL
P as
cor
e K
R h
as b
een.
..
•em
bodi
ed in
IB
M B
RM
L 1
.0 ..
2.1
[m
id-9
9 to
mid
-00]
•im
plem
ente
d in
IB
M C
omm
onR
ules
1.0
.. 2
.1
•L
imita
tions
:
–1-
vend
or
–sh
allo
w: X
ML
/Web
mec
hani
sms/
con
cept
ualiz
atio
n
–sh
allo
w: o
ntol
ogie
s
8/13
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Ben
jam
in G
roso
f M
IT
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s re
serv
ed
Bus
ines
s R
ules
Mar
kup
Lan
guag
e:T
rans
lato
rs;
Rel
atio
n to
Ind
ustr
y St
anda
rds
Dra
fts.
•<
clp>
• <
erul
e ru
lela
bel=
"lea
dTim
eRul
e1">
•
<he
ad>
•
<
clite
ral p
redi
cate
="o
rder
Mod
ific
atio
nNot
ice"
>
•
<
vari
able
nam
e="?
Ord
er"/
>
•
<
func
tion
nam
e="d
ays1
4"/>
•
<
/clit
eral
>
•
</h
ead>
•
<bo
dy>
•
<
and>
•
<
fclit
eral
pre
dica
te=
"pre
ferr
edC
usto
mer
Of"
>
•
<va
riab
le n
ame=
"?B
uyer
"/>
•
<va
riab
le n
ame=
"?Se
ller"
/>
•
<
/fcl
itera
l>
•
<
fclit
eral
pre
dica
te=
"pur
chas
eOrd
er">
•
<va
riab
le n
ame=
"?O
rder
"/>
•
<va
riab
le n
ame=
"?B
uyer
"/>
•
<va
riab
le n
ame=
"?Se
ller"
/>
•
<
/fcl
itera
l>
•
<
/and
>
•
</b
ody>
• <
/eru
le>
•...
•<
/clp
>
Com
mon
Rul
es in
clud
es
sam
ple
tran
slat
ors
to
3 ru
le s
yste
ms
(inc
l. X
SB, S
mod
els)
& K
IF.
BR
ML
⊇ {
AN
SI-d
raft
KIF
sub
set }
.
BR
ML
is c
onte
nt la
ngua
ge f
or X
ML
-ifi
ed F
IPA
Age
nt C
omm
unic
atio
n L
angu
age.
Cur
rent
-ver
sion
IB
M C
omm
onR
ules
app
N
app
1
app
2
com
pile
rin
terl
ingu
a
pars
ing/
tran
slat
ing
in &
out
deep
sha
red
sem
antic
sin
com
mon
rep
rese
ntat
ion:
com
mon
cor
es
Log
icPr
ogra
m
fam
ily
XR
ule
fam
ily
YR
ule
fam
ily
rule
sys
1
rule
sys
2
rule
sys
N
Het
erog
eneo
us
cour
teou
s
ordi
nary
/van
illa
repr
esen
tatio
n
mut
expr
iori
ties
!
repr
esen
tatio
n
{eq
uiva
lent
sem
anti
call
y
KR
obj’
s
stri
ng
XSB
form
ats
Smod
els
CR
.co
urte
ous
cour
teou
s
Log
. Pro
g.
situ
ated
cou
rteo
us L
P’s
BR
ML
,
KIF
,
engi
ne:
forw
ard
situ
ated
LP
rule
sys
tem
s
obje
cts
othe
r
8/13
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jam
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serv
ed
Rul
eML
Ini
tiat
ive
[8/0
0..p
rese
nt]
take
s B
RM
L a
s po
int o
f dep
artu
re
•L
imita
tions
of
BR
ML
:
–1-
vend
or
–sh
allo
w: X
ML
/Web
mec
hani
sms/
con
cept
ualiz
atio
n
–sh
allo
w: o
ntol
ogie
s
•R
uleM
L: [
DT
D V
0.7
1/01
, V
0.8
7/01
]
–in
depe
nden
t of
any
one
vend
or
–de
epen
wrt
XM
L/W
ebm
echa
nism
s/co
ncep
tual
izat
ion
8/13
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jam
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roso
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IT
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s re
serv
ed
Rul
eML
Ini
tiat
ive
Org
aniz
atio
n
•O
rgan
izat
ion
curr
ently
info
rmal
•A
imin
g to
mor
ph in
to W
3C a
ctiv
ity, i
f po
ssib
le
•A
doz
en o
r so
“pa
rtic
ipan
t” in
stitu
tions
fro
m e
ach
ofac
adem
e an
d in
dust
ry
•A
lot o
f m
inds
hare
alr
eady
: W
3C, D
AM
L; B
OF’
s
•ht
tp://
ww
w.m
it.ed
u/~b
gros
of/#
XM
LR
ules
, poi
nts
atht
tp://
ww
w.d
fki.d
e/ru
lem
l
8/13
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
Rul
eML
has
som
e F
irst
Ste
ps o
fW
ebiz
ing
Rul
e K
R•
UR
Is f
or lo
gica
l voc
abul
ary
and
know
ledg
e su
bset
s–
Rul
eML
V0.
8: p
redi
cate
s, f
unct
ions
, rul
es, r
uleb
ases
–R
uleM
L V
0.8:
lab
els
for
rule
s/ru
leba
ses
•Su
ppor
t RD
F:
–R
uleM
L V
0.8:
•sy
ntax
: m
ostly
uno
rder
edne
ss o
f gr
aph
•…
with
exp
licit
orde
redn
ess
•pa
rtia
l fir
st d
raft
s of
alte
rnat
ive
RD
F sy
ntax
•Su
ppor
t evo
lutio
n an
d tig
ht d
escr
iptio
n of
KR
exp
ress
ive
clas
ses:
–R
uleM
L S
ynta
x de
fine
d as
gen
eral
izat
ion-
spec
ializ
atio
n la
ttice
of D
TD
’s•
uses
XM
L e
ntity
mec
hani
sm
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
Rul
eML
’s F
irst
Ste
ps o
f Web
izin
gR
ule
KR
(co
ntin
ued)
•E
xplo
rato
ry f
eatu
res
in R
uleM
L 0
.8 [
FE
ED
BA
CK
PL
EA
SE!]
:
–m
eta
“rol
e” c
onve
ntio
n in
DT
D:
to a
id R
DF
-fri
endl
ines
s
–ar
gum
ent “
role
s” f
or a
tom
/term
arg
umen
t lis
ts
•st
ep to
war
d O
O s
uppo
rt a
nd R
DF
sup
port
•R
uleM
L T
ools
beg
inni
ng to
app
ear
–se
vera
l lin
ks o
n w
ebsi
te
8/13
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jam
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IT
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s re
serv
edNex
t Ste
ps•
RD
F v
ersi
on o
f sy
ntax
--
pla
nned
for
Rul
eML
soo
n
•X
ML
Sch
ema
vers
ion
of S
ynta
x sp
ecif
icat
ion
-- p
lann
ed f
orR
uleM
L s
oon
•Si
tuat
ed C
ourt
eous
LP
DT
D f
or R
uleM
L -
- m
y dr
aft,
soon
pub
lic
•Im
plem
enta
tion
of tr
ansl
atio
n an
d in
fere
ncin
g
–M
IT S
loan
has
wor
k in
pro
gres
s
–IB
M h
as a
nnou
nced
it w
ill s
uppo
rt in
Com
mon
Rul
es V
3
•“H
eade
r” m
eta-
data
–sp
ecif
y K
R in
cl. e
xpre
ssiv
e/sy
ntac
tic r
estr
ictio
ns
8/13
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
New
s fr
om W
3C•
Info
rmal
ly A
nnou
nced
by
Tim
Ber
ners
-Lee
at D
AM
L m
tg 7
/20/
01
•In
tern
est S
ub-G
roup
with
dis
cuss
ion
list f
orm
ing
with
in th
e W
3CSe
man
tic W
eb A
ctiv
ity
•M
issi
on s
tate
men
t bei
ng d
raft
ed a
s w
e sp
eak
•G
oal:
cre
ate
char
ter
and
cons
ensu
s fo
r po
tent
ial W
3C W
orki
ngG
roup
on
Rul
es, w
ithin
Sem
antic
Web
Act
ivity
–m
aybe
for
m W
3C S
W R
ules
WG
in 6
mon
ths
–si
blin
g of
soo
n-to
-be-
form
ed W
3C S
W W
G o
n O
ntol
ogie
s
•C
onta
ct m
e by
em
ail i
f in
tere
sted
.
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Ben
jam
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roso
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IT
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right
s re
serv
ed
My
Cur
rent
Rel
ated
Res
earc
h
•C
ombi
ne O
ntol
ogie
s (c
f. D
AM
L+
OIL
) w
ith R
ules
–[j
oint
with
DA
ML
’ers
and
oth
ers]
•“D
istr
ibut
ed B
elie
f T
rans
fer”
•U
se f
or W
eb S
ervi
ces
•A
pplic
atio
ns, i
ncl.
cont
ract
s as
rul
eset
s
8/13
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jam
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roso
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IT
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right
s re
serv
ed
•T
hank
s!
•Q
uest
ions
?
•Fo
r M
ore
Info
:
–ht
tp://
ww
w.m
it.ed
u/~b
gros
of →
#X
ML
Rul
es
8/13
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Ben
jam
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IT
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s re
serv
ed
OP
TIO
NA
L S
LID
ES
FO
LL
OW
8/13
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Ben
jam
in G
roso
f M
IT
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right
s re
serv
ed
Cur
rent
Use
s of
Rul
es in
E-B
usin
ess
•In
fere
ncin
g in
–bu
sine
ss r
ules
–w
orkf
low
–da
taba
se q
ueri
es a
nd tr
igge
rs–
inte
llige
nt a
gent
s, K
B s
yste
ms
•T
rans
form
atio
n in
(XM
L)
docu
men
t tra
nsla
tion
•Id
enti
fied
as
a D
esig
n Is
sue
of th
e W
3C S
eman
tic
Web
8/13
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Ben
jam
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roso
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IT
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right
s re
serv
ed
Aut
omat
ing
Con
trac
ting
•“C
ontr
act”
in b
road
sen
se:
= o
ffer
ing
or a
gree
men
t.
•“A
utom
ate”
in d
eep
sens
e:
=
–1.
Com
mun
icat
able
aut
omat
ical
ly.
–2.
Exe
cuta
ble
with
in a
ppro
pria
te c
onte
xt o
f co
ntra
ctin
gpa
rtie
s’ b
usin
ess
proc
esse
s.
–3.
Eva
luab
le a
utom
atic
ally
by
cont
ract
ing
part
ies.
•“r
easo
n ab
out i
t”.
–4.
Mod
ifia
ble
auto
mat
ical
ly b
y co
ntra
ctin
g pa
rtie
s.
•ne
gotia
tion,
auc
tions
.
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
Idea
/Vis
ion
#1:
Rul
e-ba
sed
Con
trac
ts fo
r E
-com
mer
ce
•R
ules
as
way
to s
peci
fy (
part
of)
bus
ines
s pr
oces
ses,
polic
ies,
pro
duct
s: a
s (p
art o
f) c
ontr
act t
erm
s.
•C
ompl
ete
or p
artia
l con
trac
t.
–A
s de
faul
t rul
es. U
pdat
e, e
.g.,
in n
egot
iatio
n.
•R
ules
pro
vide
hig
h le
vel o
f co
ncep
tual
abs
trac
tion.
–ea
sier
for
non
-pro
gram
mer
s to
und
erst
and,
spe
cify
,dy
nam
ical
ly m
odif
y &
mer
ge.
E.g
.,
–by
mul
tiple
aut
hors
, cro
ss-e
nter
pris
e, c
ross
-app
licat
ion.
•E
xecu
tabl
e. I
nteg
rate
with
oth
er r
ule-
base
d bu
sine
sspr
oces
ses.
8/13
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Ben
jam
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roso
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IT
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right
s re
serv
ed
Exa
mpl
es o
f Rul
es in
Con
trac
ts
•T
erm
s &
con
ditio
ns, e
.g.,
pric
e di
scou
ntin
g.
•Se
rvic
e pr
ovis
ions
, e.g
., ru
les
for
refu
nds.
•Su
rrou
ndin
g bu
sine
ss p
roce
sses
, e.g
., le
ad ti
me
to o
rder
.
•Pr
ice
vs. q
uant
ity v
s. d
eliv
ery
date
.
•C
ance
llatio
ns.
•D
isco
untin
g fo
r gr
oups
.
•Pr
oduc
t cat
alog
s: p
rope
rtie
s, c
ondi
tiona
l on
othe
r pr
oper
ties.
•C
redi
twor
thin
ess,
trus
twor
thin
ess,
aut
hori
zatio
n.
8/13
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Ben
jam
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roso
f M
IT
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s re
serv
ed
Con
trac
t Rul
esac
ross
App
lica
tion
s / E
nter
pris
es
App
licat
ion
1, e
.g.,
sel
ler
e-st
oref
ront
App
licat
ion
2, e
.g.,
buye
r sh
opbo
t age
nt
Bus
ines
sL
ogic
Bus
ines
sL
ogic
Rul
esR
ules
Con
trac
t Rul
es
Inte
rcha
nge
e.g
., O
PS5
e.g.
, Pro
log
“E-B
usin
ess”
“E-B
usin
ess”
“E-C
omm
erce
”
Con
trac
ting
par
ties
inte
grat
e e-
busi
ness
es v
ia s
hare
d ru
les.
8/13
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Ben
jam
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roso
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IT
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s re
serv
ed
Rul
eML
: O
vera
ll G
oals
zPr
ovid
e a
basi
s fo
r a
stan
dard
ized
rul
e m
arku
p ap
proa
ch,
with
dec
lara
tive
know
ledg
e re
pres
enta
tion
(KR
) sem
antic
sz
Aid
inte
grat
ion
of h
eter
ogen
eous
rul
e sy
stem
s an
d ap
plic
atio
ns,
via
shar
ed r
ule
mar
kup
lang
uage
zSt
art w
ith c
omm
erci
ally
impo
rtan
t fla
vors
of
rule
sz
Com
plem
ent X
ML
ont
olog
ies
alre
ady
evol
ving
for
var
ious
dom
ains
zSt
art s
impl
e w
ith a
ker
nel K
R, t
hen
add
exte
nsio
nsin
crem
enta
lly.
zB
ecom
e an
indu
stry
sta
ndar
d (e
.g. v
ia W
3C)
8/13
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jam
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roso
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IT
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right
s re
serv
ed
Tec
hnic
al A
ppro
ach
of R
uleM
Lz
Star
t with
: D
atal
og L
ogic
Pro
gram
s w
ith r
ules
labe
led
as
kern
el
zsi
mila
r to
Bus
ines
s R
ules
Mar
kup
Lan
guag
e (I
BM
Com
mon
Rul
es)
zA
dd:
expr
essi
ve e
xten
sion
s/re
stri
ctio
ns,
UR
I’s
zne
gatio
n-as
-fai
lure
(w
ell-
foun
ded
sem
antic
s); c
lass
ical
neg
atio
n
zpr
iori
tized
con
flic
t han
dlin
g cf
. Cou
rteo
us L
ogic
Pro
gram
s (s
tays
trac
tabl
e!)
zm
odul
ar r
ules
ets;
m
odul
ar c
ompi
ler
to O
rdin
ary
Log
ic P
rogr
ams
zpr
oced
ural
atta
chm
ents
: ac
tions
, qu
erie
s ;
cf.
Situ
ated
Log
ic P
rogr
ams
zlo
gica
l fun
ctio
ns:
sta
ndar
d bu
ilt-i
ns,
user
-def
ined
z1s
t-or
der
logi
c ty
pe e
xpre
ssiv
enes
s cf
. Llo
yd L
P’s,
DA
ML
+OIL
, KIF
zm
ore:
equ
ival
ence
/rew
ritin
g ru
les;
...
tem
pora
l, B
ayes
ian,
fuz
zy, …
zFa
mily
of
DT
D’s
: a
gene
raliz
atio
n-sp
ecia
lizat
ion
hier
arch
y (l
attic
e)
zde
fine
DT
D’s
mod
ular
ly, u
sing
XM
L e
ntiti
es (
~mac
ros)
zop
tiona
l hea
der
to d
escr
ibe
expr
essi
ve-c
lass
usi
ng “
met
a-”o
ntol
ogy
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jam
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IT
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s re
serv
ed
Dec
lara
tive
Sem
anti
cs a
t Cor
e
•D
esir
e: d
eep
sem
antic
s (m
odel
-the
oret
ic)
to
–un
ders
tand
and
exe
cute
impo
rted
rul
es.
•Po
ssib
le o
nly
for
shar
ed e
xpre
ssiv
e su
bset
s: “
core
s”.
–R
est t
rans
late
d w
ith
supe
rfic
ial s
eman
tics
.
•A
ppro
ach:
dec
lara
tiven
ess
of c
ore
/ rep
’n (
in s
ense
of
know
ledg
ere
pres
enta
tion
theo
ry).
–A
giv
en s
et o
f pr
emis
es e
ntai
ls a
set
of
sanc
tione
d co
nclu
sion
s.In
depe
nden
t of
impl
emen
tatio
n &
infe
renc
ing
cont
rol (
bkw
vs.
fw
d).
–M
axim
izes
ove
rall
adva
ntag
es o
f ru
les:
•N
on-p
rogr
amm
ers
unde
rsta
nd &
mod
ify.
•D
ynam
ical
ly (
run-
time)
mod
ify.
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jam
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IT
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s re
serv
ed
Inte
rlin
gua:
Nee
d G
o B
eyon
d K
IF
•K
IF h
as m
ajor
lim
itatio
ns:
–lo
gica
lly m
onot
onic
.•
yet v
irtu
ally
all
prac
tical
rul
e (a
ndpr
obab
ility
) sy
stem
s ar
e no
n-m
onot
onic
.
–pu
re-b
elie
f, n
o pr
oced
ural
atta
chm
ents
.•
yet m
ost p
ract
ical
rul
e sy
stem
s do
invo
kepr
oced
ures
ext
erna
l to
the
infe
renc
e en
gine
.
•C
andi
date
s to
com
plem
ent K
IF e
xist
:
–lo
gic
prog
ram
s, B
ayes
net
s, ..
.
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jam
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IT
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s re
serv
ed
Rul
eML
: F
urth
er D
irec
tion
s
•m
ove
to X
ML
Sch
ema
base
d ra
ther
than
DT
D b
ased
•ad
ditio
nal X
ML
syn
taxe
s: R
DF;
sur
face
/"st
yle-
shee
ted"
•m
ore
KR
’s:
KIF
/cla
ssic
al, N
otat
ion
3, B
ayes
ian,
fuzz
y, r
ewri
ting,
tem
pora
l, …
•pr
ovid
e R
ule
mec
hani
sm to
em
ergi
ng W
3C s
tand
ards
:
–Se
man
tic W
eb /
RD
F, P
3P, …
8/13
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jam
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roso
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IT
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s re
serv
ed
Rul
eML
: R
elev
ant O
ther
Eff
orts
in W
3C a
nd M
arku
pz
RD
F, R
DFS
, DA
ML
(+O
IL),
Sem
antic
Web
zP3
P pr
ivac
y po
licie
s: A
PPE
L r
ules
zX
ML
Que
ry
zO
ther
s:z
XSL
T
zPr
edic
tive
Mod
el M
arku
p L
angu
age
(rul
es f
rom
dat
a m
inin
g)
8/13
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jam
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roso
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IT
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right
s re
serv
ed
Cou
rteo
us L
P’s
: th
e W
hat
•U
pdat
ing/
mer
ging
of
rule
set
s: i
s cr
ucia
l, of
ten
gene
rate
s co
nflic
t.
•C
ourt
eous
LP’
s fe
atur
e pr
iori
tized
han
dlin
g of
con
flic
ts.
•Sp
ecif
y sc
ope
of c
onfl
ict v
ia a
set
of
pai
rwis
e m
utua
l exc
lusi
on c
onst
rain
ts.
–E
.g.,
⊥ ←
dis
coun
t(?p
rodu
ct,5
%)
∧ di
scou
nt(?
prod
uct,1
0%)
.
–E
.g.,
⊥ ←
loya
lCus
tom
er(?
c,?s
) ∧
prem
iere
Cus
tom
er(?
c,?s
) .
–Pe
rmit
clas
sica
l-ne
gati
on o
f at
oms:
¬p
mea
ns p
has
trut
h va
lue
fals
e
•im
plic
itly
, ⊥
← p
∧ ¬
p
for
eve
ry a
tom
p.
•P
rior
itie
s be
twee
n ru
les:
par
tially
-ord
ered
.
–R
epre
sent
pri
orit
ies
via
rese
rved
pre
dica
te th
at c
ompa
res
rule
labe
ls:
•ov
erri
des(
rule
1,ru
le2)
m
eans
rul
e1 is
hig
her-
prio
rity
than
rul
e2.
•E
ach
rule
opt
iona
lly
has
a ru
le la
bel w
hose
for
m is
a f
unct
iona
l ter
m.
•ov
erri
des
c
an b
e re
ason
ed a
bout
, jus
t lik
e an
y ot
her
pred
icat
e.
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jam
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roso
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s re
serv
ed
Pri
orit
ies
are
avai
labl
e an
d us
eful
•Pr
iori
ty in
form
atio
n is
nat
ural
ly a
vaila
ble
and
usef
ul.
E.g
.,–
rece
ncy:
hig
her
prio
rity
for
mor
e re
cent
upd
ates
.
–sp
ecif
icity
: hi
gher
pri
orit
y fo
r m
ore
spec
ific
cas
es (
e.g.
, exc
epti
onal
cas
es,
sub-
case
s, in
heri
tanc
e).
–au
thor
ity:
hig
her
prio
rity
for
mor
e au
thor
itat
ive
sour
ces
(e.g
., le
gal
regu
latio
ns, o
rgan
izat
iona
l im
pera
tive
s).
–re
liabi
lity
: hi
gher
pri
ority
for
mor
e re
liab
le s
ourc
es (
e.g.
, sec
urity
cert
ific
ates
, via
-del
egat
ion,
ass
umpt
ions
, obs
erva
tiona
l dat
a).
–cl
osed
wor
ld:
low
est p
rior
ity f
or c
atch
-cas
es.
•M
any
prac
tical
rul
e sy
stem
s em
ploy
pri
oriti
es o
f so
me
kind
, oft
enim
plic
it, e
.g.,
–ru
le s
eque
ncin
g in
Pro
log
and
prod
uctio
n ru
les.
•co
urte
ous
subs
umes
this
as
spec
ial c
ase
(tot
ally
-ord
ered
pri
orit
ies)
,pl
us e
nabl
es:
mer
ging
, mor
e fl
exib
le &
pri
ncip
led
trea
tmen
t.
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jam
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s re
serv
ed
Set o
f U
nref
uted
Can
dida
tes
for
p1,..
.,pk:
Tea
m f
or p
1, ..
., T
eam
for
pk
Run
Rul
es f
or p
1,...
,pk
Set o
f C
andi
date
s fo
r p1
,...,p
k:T
eam
for
p1,
...,
Tea
m f
or p
k
Prio
ritiz
ed R
efut
atio
n
Skep
ticis
m
Con
clud
e W
inni
ng S
ide
if a
ny: a
t mos
t one
of
{p1,
...,p
k}
Con
clus
ions
fro
m o
ppos
ition
-loc
ales
pre
viou
s to
this
opp
ositi
on-l
ocal
e {p
1,...
,pk}
Pri
orit
ized
arg
umen
tati
on in
an
oppo
siti
on-l
ocal
e.
(Eac
h pi
is a
gro
und
clas
sica
l lit
eral
. k
≥ 2.
)
8/13
/200
1by
Ben
jam
in G
roso
f M
IT
copy
right
s re
serv
ed
Sit
uate
d L
P’s
: O
verv
iew
•Po
int o
f de
part
ure:
LP’
s ar
e pu
re-b
elie
f re
pres
enta
tion,
but
mos
tpr
actic
al r
ule
syst
ems
wan
t to
invo
ke e
xter
nal p
roce
dure
s.
•Si
tuat
ed L
P ‘s
fea
ture
a s
eman
tical
ly-c
lean
kin
d of
pro
cedu
ral
atta
chm
ents
. I.
e., t
hey
hook
bel
iefs
to d
rive
pro
cedu
ral A
PI’s
out
side
the
rule
eng
ine.
•Pr
oced
ural
atta
chm
ents
for
sen
sing
(qu
erie
s) w
hen
test
ing
anan
tece
dent
con
ditio
n or
for
eff
ecti
ng (
actio
ns)
upon
con
clud
ing
aco
nseq
uent
con
ditio
n. A
ttach
ed p
roce
dure
is in
voke
d w
hen
test
ing
orco
nclu
ding
in in
fere
ncin
g.
•Se
nsor
or
effe
ctor
link
sta
tem
ent s
peci
fies
an
asso
ciat
ion
from
apr
edic
ate
to a
pro
cedu
ral c
all p
atte
rn, e
.g.,
a m
etho
d.
A li
nk is
spec
ifie
d as
par
t of
the
rep
rese
ntat
ion.
I.e
., a
SLP
is a
con
duct
set
that
incl
udes
link
s as
wel
l as
rule
s.
8/13
/200
1by
Ben
jam
in G
roso
f M
IT
copy
right
s re
serv
ed
Sum
mar
y:C
ourt
eous
(Si
tuat
ed)
LP
’s a
s C
ore
KR
•K
ey O
bser
vatio
ns a
bout
Dec
lara
tive
OL
P:
–ca
ptur
es c
omm
on c
ore
amon
g co
mm
erci
ally
impo
rtan
t rul
e sy
stem
s.
–is
exp
ress
ive,
trac
tabl
e, f
amili
ar.
–ad
vant
ages
com
pare
d to
cla
ssic
al lo
gic
/ AN
SI-d
raft
KIF
:
• +
+ lo
gica
l non
-mon
oton
icity
, neg
atio
n-as
-fai
lure
.
• −
− d
isju
ncti
ve c
oncl
usio
ns.
• +
+ tr
acta
ble.
• +
+ p
roce
dura
l atta
chm
ents
: Si
tuat
ed L
P’s.
•C
leve
rnes
s of
Cou
rteo
us e
xten
sion
to th
e O
LP
repr
esen
tatio
n:
–pr
iori
tize
d co
nfli
ct h
andl
ing
→
mod
ular
ity in
spe
cifi
catio
n. A
nd c
onsi
sten
cy.
–co
urte
ous
com
pile
r →
mod
ular
ity in
sof
twar
e en
gine
erin
g.
–m
utex
’s &
con
flic
t loc
ales
→
kee
p tr
acta
bilit
y. (
Com
pile
r is
O(n
^3).
)