Drools Expert talk given at JUDCon India 2012

1. 1
Mark
Proctor
Project Lead
The SkyNet funding bill is passed.
The system goes online on August 4th, 1997.
Human decisions are removed from strategic defense.
SkyNet begins to learn at a geometric rate.
It becomes self-aware at 2:14am Eastern time, August 29th
In a panic, they try to pull the plug.
And, Skynet fights back

2. 2
Wumpus World

3. 3
Wumpus World

4. 4
Wumpus World
C e ll H e ro Wu m p u s P itt G old
int row int row int row int row int row
Int col Int col Int col Int col Int col

5. 5
Wumpus World

6. 6
Wumpus World

7. 7
Wumpus World
d e m ons tration

8. 8
Drools B ooks

9. 9
S ample Indus tries and Us ers
Inve s tm e nt
Mille nniu m Inve s tm e nt G rou p (M IG )
Logis tics
Fed ex
Airline
S ab re
M ortgage
F ranklin Am e rican
H e alth care
OSDE

10. 10
B oot C amps
S an F rancis co 2009 (40+ atte nd e e s )
S p ons ore d b y Th ird P illar
S u n, F AM C , O S D E , Kas e ya, F e d e x, TU G rou p , Inte rm ou ntain
H e alth care , G ap , S ony P ictu re s , Lockh e e d Martin, Kais e r, H P ,
We lls F argo, U S N avy R e s e arch , F O LIO fn, Boe ing .....
S an D ie go 201 0 (80+ atte nd e s s )
S p ons ore d b y U S N avy
5 d ay e ve nt, with 2 d ays focu s on th e h e alth care ind u s try
O S D E , AT&T, S AIC , U S N avy R e s e arch , Kais e r, C linica,
Inte rm ou ntain H e alth care , G E H e alth care , VA, Boe ing,
N ationwid e ....

11. 11
Integrated S ys tems
Rules Rules Workflows Workflows
Event
Processes
Semantic
Ontologies
Semantic
Event Ontologies
Processes

12. 12
generic Rules and proces s es
?
Decision
Services
SCOPE
Process
specific
Rules
tightly coupled COUPLING loosely coupled

13. 13
Integrated S ys tems
Drools JBPM5 Drools Drools
Expert (Drools Flow) Fusion Guvnor
Drools Drools Drools Drools
Planner Grid Semantics Chance
Business Logic integration System

14. 14
Declarative Programming
P rod u ction R u le S ys te m s P R D (forward ch aining)
R e active
wh e n Alarm ( s tatu s = = “ale rt” )
th e n s e nd ( “warning” )
Logic P rogram m ing LP (b ackward ch aining)
Q u e ry
d e s ce nd ant( “m ary”, “j ”)
ane
F u nctional P rogram m ing F P
Map ,F old , F ilte r
avg([1 2, 1 6, 4, 6])
R e tu rns s ingle valu e 9.5
rou nd ([1 0.3, 4.7, 7.8] )
R e tu rns Lis t [1 0, 5, 8]
D e s crip tion Logic
P e rs on H as N am e and

15. 15
C las s es
C a s h f lo w
A cco u n t
D a te d a te
lo n g a c c o u n t N o
d o u b le a m o u n t
d o u b le b a la n c e
in t t y p e
lo n g a c c o u n t N o
A c c o u n t in g P e r io d
D a te s ta r t
D a te e n d

16. 16
C redit C as hflow Rule
select * from Account acc,
Cashflow cf, AccountPeriod ap
where acc.accountNo == cf.accountNo and
cf.type == CREDIT
cf.date >= ap.start and
cf.date <= ap.end
trigger : acc.balance += cf.amount
rule “increase balance for AccountPeriod Credits”
when
ap : AccountPeriod()
acc : Account( $accountNo : accountNo )
CashFlow( type == CREDIT,
accountNo == $accountNo,
date >= ap.start && <= ap.end,
$ammount : ammount )
then
acc.balance += $amount;
end

17. 17
Rules as a “ view”
CashFlow AccountingPeriod
date amount type accountNo start end
12-Jan-07 100 CREDIT 1 01-Jan-07 31-Mar-07
2-Feb-07 200 DEBIT 1
18-May-07 50 CREDIT 1 Account
9-Mar-07 75 CREDIT 1 accountNo balance
1 0
rule “increase balance for AccountPeriod rule “decrease balance for AccountPeriod
Credits” Debits”
when when
ap : AccountPeriod() ap : AccountPeriod()
acc : Account( $accountNo : accountNo ) acc : Account( $accountNo : accountNo )
CashFlow( type == CREDIT, CashFlow( type == DEBIT,
accountNo == $accountNo, accountNo == $accountNo,
date >= ap.start && <= ap.end, date >= ap.start && <= ap.end,
$ammount : ammount ) $ammount : ammount )
then then
acc.balance += $amount; acc.balance -= $amount;
end CashFlow end CashFlow
date amount type date amount type
12-Jan-07 100 CREDIT 2-Feb-07 200 DEBIT
9-Mar-07 75 CREDIT
Account
accountNo balance
1 -25

18. 18
Definitions
p u b lic clas s Ap p licant {
p rivate S tring nam e ;
p rivate int age ;
p rivate b oole an valid ;
/ ge tte r and s e tte r m e th od s h e re
/
}
rule "Is of valid age" when
$a : Applicant( age < 18 )
then
modify( $a ) { valid = false };
ends

19. 19
B uilding
Knowle d ge Bu ild e r kb u ild e r = Knowle d ge Bu ild e rF actory.ne wK nowle d ge Bu ild e r();
kb u ild e r.ad d ( R e s ou rce F actory
.ne wC las s P ath R e s ou rce ( "lice ns e Ap p lication.d rl", ge tC las s () ),
R e s ou rce Typ e .D R L );
if ( kb u ild e r.h as E rrors () ) {
S ys te m .e rr.p rintln( kb u ild e r.ge tE rrors ().toS tring() );
}
kb as e .ad d K nowle d ge P ackage s ( kb u ild e r.ge tKnowle d ge P ackage s () );

20. 20
S pring C onfiguration

21. 21
E xecuting
rule "Is of valid age" when
$a : Applicant( age < 18 )
then
modify( $a ) { valid = false };
ends
S tate le s s K nowle d ge S e s s ion ks e s s ion = kb as e .ne wS tate le s s Knowle d ge S e s s ion();
Ap p licant ap p licant = ne w Ap p licant( "M r Joh n S m ith ", 1 6 );
as s e rtTru e ( ap p licant.is Valid () );
ks e s s ion.e xe cu te ( ap p licant );
as s e rtF als e ( ap p licant.is Valid () );

22. 22
Definitions
p u b lic clas s R oom {
p rivate S tring nam e
/ ge tte r and s e tte r m e th od s h e re
/
}
p u b lic clas s S p rinkle r {
p rivate R oom room ;
p rivate b oole an on;
/ ge tte r and s e tte r m e th od s h e re
/
}
p u b lic clas s F ire {
p rivate R oom room ;
/ ge tte r and s e tte r m e th od s h e re
/
}
p u b lic clas s Alarm {

23. 23
Definitions
ru le "Wh e n th e re is a fire tu rn on th e s p rinkle r" wh e n
F ire ($room : room )
$s p rinkle r : S p rinkle r( room = = $room , on = = fals e )
th e n
m od ify( $s p rinkle r ) { on = tru e };
p rintln( "Tu rn on th e s p rinkle r for room " + $room .nam e );
e nd
ru le "Wh e n th e fire is gone tu rn off th e s p rinkle r" wh e n
$room : R oom ( )
$s p rinkle r : S p rinkle r( room = = $room , on = = tru e )
not F ire ( room = = $room )
th e n
m od ify( $s p rinkle r ) { on = fals e };
p rintln( "Tu rn off th e s p rinkle r for room " + $room .nam e );

24. 24
Definitions
ru le "R ais e th e alarm wh e n we h ave one or m ore fire s " wh e n
e xis ts F ire ()
th e n
ins e rt( ne w Alarm () );
p rintln( "R ais e th e alarm " );
e nd
ru le "C ance l th e alarm wh e n all th e fire s h ave gone " wh e n
not F ire ()
$alarm : Alarm ()
th e n
re tract( $alarm );
p rintln( "C ance l th e alarm " );
e nd

25. 25
Definitions
ru le "S tatu s ou tp u t wh e n th ings are ok" wh e n
not Alarm ()
not S p rinkle r( on = = tru e )
th e n
p rintln( "E ve ryth ing is ok" );
e nd

26. 26
E xecuting
S tring[] nam e s = ne w S tring[]{"kitch e n", "b e d room ", "office ", "livingroom "};
M ap < S tring,R oom > nam e 2room = ne w H as h M ap < S tring,R oom > ();
for( S tring nam e : nam e s ){
R oom room = ne w R oom ( nam e );
nam e 2room .p u t( nam e , room );
ks e s s ion.ins e rt( room );
S p rinkle r s p rinkle r = ne w S p rinkle r( room );
ks e s s ion.ins e rt( s p rinkle r );
}
ks e s s ion.fire AllR u le s ()
> E ve ryth ing is ok

27. 27
E xecuting
F ire kitch e nF ire = ne w F ire ( nam e 2room .ge t( "kitch e n" ) );
F ire office F ire = ne w F ire ( nam e 2room .ge t( "office " ) );
F actH and le kitch e nF ire H and le = ks e s s ion.ins e rt( kitch e nF ire );
F actH and le office F ire H and le = ks e s s ion.ins e rt( office F ire );
ks e s s ion.fire AllR u le s ();
> R ais e th e alarm
> Tu rn on th e s p rinkle r for room kitch e n
> Tu rn on th e s p rinkle r for room office

28. 28
E xecuting
ks e s s ion.re tract( kitch e nF ire H and le );
ks e s s ion.re tract( office F ire H and le );
ks e s s ion.fire AllR u le s ()
> Tu rn off th e s p rinkle r for room office
> Tu rn off th e s p rinkle r for room kitch e n
> C ance l th e alarm
> E ve ryth ing is ok
ru le "S tatu s ou tp u t wh e n th ings are ok" wh e n
not Alarm ()
not S p rinkle r( on = = tru e )
th e n
p rintln( "E ve ryth ing is ok" );

29. 29
C onditional E lements
not Bus( color = “red” )
exists Bus( color = “red” )
forall ( $bus : Bus( color == “red” ) )
forall ( $bus : Bus( floors == 2 )
Bus( this == $bus, color == “red” ) )

30. 30
A ccumulate C E
ru le "accu m u late "
wh e n
$s u m : N u m b e r( intValu e > 1 00 )
from accu m u late ( Bu s ( color = = "re d ", $t : takings )
s u m ( $t ) )
th e n
p rint "s u m is “ + $s u m ;
e nd

31. 31
Decis ion Table

32. 32
Decis ion Table
rule "Pricing bracket_10"
when
Driver(age >= 18, age <= 24,
locationRiskProfile == "LOW",
priorClaims == "1")
policy:
Policy(type == "COMPREHENSIVE")
then
policy.setBasePrice(450);

33. 33
Types
Types
Layou t
H orizontal
Ve rtical
Lim ite d e ntry
E xte nd e d e ntry
C ate goris ation
E xp and e d form , contracte d form
M u lti-h it, all h its
M u lti-h it, firs t h it
S ingle h it

34. 34
Layouts
Horizontal

35. 35
Layouts
Vertical

36. 36
Layouts
Limited entry

37. 37
Layouts
E xtended entry

38. 38
C ategoris ation
E xpanded form
S ingle colu m n for e ve ry cond ition com b ination
Th e nu m b e r of colu m ns s h ou ld e qu al th e p rod u ct of
th e nu m b e r of s tate s for e ve ry cond ition.
e .g. 2 cond itions , one with 3 s tate s th e oth e r 4 (s e e ab ove ):
3 * 4 = 1 2 com b inations
e .g. 2 cond itions e ach with 3 s tate s and 1 cond ition with 4 s tate s give s :
3 * 3 * 4 = 36 com b inations

39. 39
C ategoris ation
E xpanded form (continued)

40. 40
C ategoris ation
C ontracted form
C ontraction is th e firs t op tim is ation.
R e d u ce s th e nu m b e r of cond ition colu m ns .
R e m ove s im p os s ib le com b inations
If th e s am e actions e xis t for ru le s cove ring all
cond ition s tate s for a give n cond ition th e y can b e
com b ine d and th e cond ition s tate b e com e s
irre le vant.

41. 41
C ategoris ation
C ontracted form – s tage 1
R u le s 2 and 3 are im p os s ib le cond itions

42. 42
C ategoris ation
C ontracted form – s tage 2
M e rge ad j nt colu m n grou p s with id e ntical action p arts
ace

43. 43
C ategoris ation
Multi-hit, all hits
To ge t com p le te re s u lt all ru le s m atch ing ne e d to h ave
th e ir actions e xe cu te d .
C ond ition colu m ns are not m u tu ally e xclu s ive
If th e C ond ition colu m ns are not e xclu s ive , s om e
com b ination of cond itions are p re s e nt in m ore th an
one colu m n, wh ich m ay le ad to am b igu ity or
incons is te ncy.

44. 44
C ategoris ation
Multi-hit, all hits
G ive n a 35 ye ar old with 1 5 ye ars s e rvice
1 . R u le 1 m atch e s , giving 22 d ays
2. R u le 5 m atch e s , giving an ad d itional 3 d ays
3. A total of 25 d ays is as s igne d

45. 45
C ategoris ation
Multi-hit, firs t hit
To ge t com p le te re s u lt th e firs t ru le (from le ft-to-righ t)
m atch ing ne e d s to h ave its action e xe cu te d .
C ond ition colu m ns are not m u tu ally e xclu s ive .
If th e C ond ition colu m ns are not e xclu s ive , s om e
com b ination of cond itions are p re s e nt in m ore th an
one colu m n, wh ich m ay le ad to am b igu ity or
incons is te ncy.

46. 46
C ategoris ation
Multi-hit, firs t hit
G ive n a 35 ye ar old with 1 5 ye ars s e rvice
1 . R u le 4 m atch e s , giving 25 d ays
2. R u le 6 wou ld m atch b u t R u le 4 was th e firs t h it

47. 47
C ategoris ation
S ingle hit
E ach p os s ib le com b ination of cond itions m atch e s
e xactly one , and only one , ru le .
C ond ition colu m ns are m u tu ally e xclu s ive
As th e C ond ition colu m ns are e xclu s ive ; com b inations
of cond itions cannot b e p re s e nt in m ore th an one
colu m n wh ich e lim inate s am b igu ity and
incons is te ncy.
C las s ic form if S ingle -h it is "e xp and e d d e cis ion tab le ";
b u t th is can b e op tim is e d or “contracte d ”.

48. 48
C ategoris ation
S ingle hit
G ive n a 35 ye ar old with 1 5 ye ars s e rvice
1 . R u le 3 m atch e s , giving 25 d ays
2. N o oth e r ru le s m atch

49. 49
Validation & Verification
Redundancy - S ubs umption

50. 50
Validation & Verification
Deficiency
P re m iu m is £500 if ap p licant age is le s s th an 30
P re m iu m is £300 if Ye ars With ou t C laim is gre ate r th an or e qu al to 1 0 ye ars .
Ap p licant is 29, p re m iu m is £500
Ap p licant h as 1 2 ye ars with ou t claim , p re m iu m is £300
Ap p licant is 29 with 1 2 ye ars with ou t claim , p re m iu m is ?!?

51. 51
Decis ion Tables in G uvnor
Vid e o d e m ons tration

52. 52
Guided Editor

53. 53
Decision Table

54. 54
Decision Table

55. 55
Decision Table

56. 56
Decision Tables – Cell merging.. etc
Cell Merging
Cell Grouping
Typed Columns
Sorting by column
Negate pattern
Support for Ohterwise

57. 57
Decision Tables – Wizard

58. 58
Rule Templates

59. 59
Rule Templates

60. 60
Rule Templates

61. 61
Scenario Testing

62. 62
TMS and Inference
ru le "Is s u e C h ild Bu s P as s " Couples the logic
wh e n
$p : P e rs on( age < 1 6 )
th e n
ins e rt(ne w C h ild Bu s P as s ( $p ) );
e nd
ru le "Is s u e Ad u lt Bu s P as s " What happens when the Child
stops being 16?
wh e n
$p : P e rs on( age > = 1 6 )
th e n
ins e rt(ne w Ad u ltBu s P as s ( $p ) );
e nd

63. 63
TMS and Inference
Bad
Monolith ic
Le aky
Brittle inte grity - m anu al m ainte nance

64. 64
TMS and Inference
A ru le “logically” ins e rts an ob j ct
e
Wh e n th e ru le is no longe r tru e , th e ob j ct is re tracte d .
e
wh e n
de-couples the logic
$p : P e rs on( age < 1 6 )
th e n
logicalIns e rt( ne w Is C h ild ( $p ) )
e nd
wh e n Maintains the truth by
automatically retracting
$p : P e rs on( age > = 1 6 )
th e n
logicalIns e rt( ne w Is Ad u lt( $p ) )
e nd

65. 65
TMS and Inference
ru le "Is s u e C h ild Bu s P as s "
wh e n
$p : P e rs on( )
Is C h ild ( p e rs on = $p )
th e n
logicalIns e rt(ne w C h ild Bu s P as s ( $p ) );
e nd
The truth maintenance
ru le "Is s u e Ad u lt Bu s P as s " cascades
wh e n
$p : P e rs on( age > = 1 6 )
Is Ad u lt( p e rs on = $p )
th e n
logicalIns e rt(ne w Ad u ltBu s P as s ( $p ) );
e nd

66. 66
TMS and Inference
ru le "Is s u e C h ild Bu s P as s "
wh e n
$p : P e rs on( )
not( C h ild Bu s P as s ( p e rs on = = $p ) )
th e n
re qu e s tC h ild Bu s P as s ( $p ); The truth maintenance
cascades
e nd

67. 67
TMS and Inference
G ood
D e -cou p le knowle d ge re s p ons ib ilitie s
E ncap s u late knowle d ge
P rovid e s e m antic ab s tractions for th os e e ncap s u lation
Inte grity rob u s tne s s – tru th m ainte nance

68. 68
Ques tions ?
D ave Bowm an: All righ t, H AL; I'll go in
th rou gh th e e m e rge ncy airlock.
H AL: With ou t you r s p ace h e lm e t, D ave ,
you 're going to find th at rath e r d ifficu lt.
D ave Bowm an: H AL, I won't argu e with
you anym ore ! O p e n th e d oors !
H AL: D ave , th is conve rs ation can s e rve
no p u rp os e anym ore . G ood b ye .
essor Falken.
oshua.
. The only winning move is not to play. How about a nice game of chess