2. Rule based Systems
● Expert Systems / Business
rules engine / Production
Systems / Inference Engines
are used to address rule
engines based on their
implementations and use.
● Something most people like to
have, although not many
people know why they need it.
3. Rules
● Allow users to specify the requirements/
knowledge about processing using
– Declarative (Say what should happen, not how to
do it)
– Logic based languages.
4. Types of Rules
● Four types of rules (from
http://www.w3.org/2000/10/swap/doc/rule-systems)
– Derivation or Deduction Rules – Each rules express if
some statements are true, another statement must be
true. Called logical implication. E.g. Prolog
– Transformation Rules- transform between knowledge
bases, e.g. theorem proving
– Integrity Constraints – verification rules
– Reaction or Event-Condition-Action (ECA) Rules –
includes a actions in addition to inference. e.g. Drools
5. Two types of Rule Engines
● Forward Chaining – starts with facts/data and
trigger actions or output conclusions
● Backward chaining – starts with goals and
search how to satisfy that (e.g. Prolog)
6. Production Systems
● Drools belongs to the category of rule engines called
production systems [1] (which execute actions based
on conditions)
● Drools use forward chaining[2] (start with data and
execute actions to infer more data )
● Priorities assigned to rules are used to decide the
order of rule execution
● They remember all results and use that to optimize
new derivations (dynamic programming like)
http://en.wikipedia.org/wiki/AI_production
http://en.wikipedia.org/wiki/Forward_chaining
7. Big Picture
● Usually a Rule engine
usually includes three
parts.
– Facts represented as
working memory
– Set of rules that
declaratively define
conditions or situations
e.g. Prolog route(X,Z) <-
road(X,Z)
– Actions executed or
inference derived based
on the rules
8. Why rule engines?
● Simplify complicated requirements with
declarative logic, raising the level of abstraction
of the system
● Externalize the business logic (which are too
dynamic) from comparatively static code base
● Intuitive and readable than code, easily
understood by business people/ non technical
users
http://en.wikipedia.org/wiki/AI_production
http://en.wikipedia.org/wiki/Forward_chaining
9. Why rule engines? [Contd.]
● Create complex interactions which can have
powerful results, even from simple facts and rules.
● Different approach to the problem, some problem
are much easier using rules.
● Can solve hard problems (Problems we only
understand partially)
● Fast and scalable when there is a data set that
changes little by little
10. When to use Rules?
● When there is no satisfactory traditional
programming approach to solve the problem!
● To separate code and business logic
● The problem is beyond any obvious algorithmic
solution. (not fully understood)
● When the logic changes often
● When Domain experts are none technical.
1. Real-World Rule Engines
http://www.infoq.com/articles/Rule-Engines
2. Why are business rules better than traditional code?
http://www.edmblog.com/weblog/2005/11/why_are_busines.html
3. Rules-based Programming with JBoss Rules/Drools
www.codeodor.com
11. When not to use rule engines?
● It is slower then usual code most of the time, so
unless one of the following is true is should not
be used
– Complexity of logic is hard to tackle
– Logic changes too often
– Required to use by non technical users
– It is a usecase where rules are faster.
● Interactions between rules could be quite
complex, and one mistake could change the
results drastically and unexpected way e.g.
recursive rules
● Due to above testing and debugging is required,
so if results are hard to verified it should not be
used.
12. Drools Rule Engine
● Forward chaining, production system based on
Rete algorithm.
● Written in Java, and support OOP based
model
● Open source/ Apache License compatible
● Backed up JBoss, also called JBoss rules
● Link http://jboss.org/drools
13. Model
● Facts as a Object repository of java objects
● New objects can be added, removed or updated
● support if <query> then <action> type rules
● Queries use OOP format
15. Anatomy of a Rule
● Has two parts
rule "rule-name"
when
<query in a Object query
language>
then
<any java code>
end
● When condition is satisfied, action is carried
out.
16. Business Rules
● Working Memory
– Insert, retract, update
● Stateful sessions
● Stateless sessions
● Queries -
QueryResults results = ksession.getQueryResults(
"my query", new Object[] { "string" } );
● Globals ksession.setGlobal("list", list);
● Agenda filters makes sure only selected rules are
fired. ksession.fireAllRules( new
RuleNameEndsWithAgendaFilter( "Test" ) );
17. Rete Algorithm
● Dr Charles L. Forgy's Ph.D. Thesis, 1974
● Creates a tree representing rules, and data
propagate through the tree and out put actions.
● Partial results are saved in memory.
18. A Sample Rule
Following rule increase the premium by 15% if driver is
less than 25 and drives a sport car.
rule "MinimumAge"
when
$d : Customer(age < 25)
$c : Car(vin=$d.vin && model =
“sport”)
then
c.IncreasePrimium(15);
end
19. Actions
● Any java code goes in the then clause
● Also support some level of scripts
● Operations on working memory
– update(object); will tell the engine that an object has
changed
– insert(new Something()); will place a new object of
your creation into the Working Memory.
– insertLogical(new Something()); is similar to insert,
but the object will be automatically retracted when
there are no more facts to support the truth of the
currently firing rule.
– retract(handle); removes an object from Working
Memory.
20. Rule Patterns
Following rule reject all customers whose age less than 17.
rule "MinimumAge"
when
c : Customer(age < 17)
then
c.reject();
end
Conditions support <, >, ==, <=, >=, matches / not matches,
contains / not contains. And following rules provide a discount
if customer is married or older than 25.
rule "Discount"
when
c : Customer( married == true || age > 25)
then
c.addDiscount(10);
end
21. Joins
Following rule increase the premium by 15% if driver is
less than 25 and drives a sport car.
rule "MinimumAge"
when
$d : Customer(age < 25)
$c : Car(vin=$d.vin && model =
“sport”)
then
c.IncreasePrimium(15);
end
23. OR, AND
● OR – true if either of the statements true
– E.g. Customer(age > 50) or Vehicle( year > 2000)
● AND – provide logical, if no connectivity is define
between two statements, “and” is assumed by default.
For an example.
c : Customer( timeSinceJoin > 2);
not (Accident(customerid == c.name))
and
c : Customer( timeSinceJoin > 2) and
not (Accident(customerid == c.name))
are the same.
24. eval(..)
● eval(boolean expressions) – with eval(..) any
Boolean expression can be used.
– E.g. C:Customer(age > 20)
eval(C.calacuatePremium() > 1000)
● Can not hold partial results for eval(..) so
Drools going to recalculate eval(..)
statements and what ever trigged from that.
● So it is going to be inefficient, so use
sparingly
25. Not
● Not – negation or none can be found. E.g.
not Plan( type = “home”)
is true if no plan of type home is found. Following
is true if customer has take part in no accidents.
rule "NoAccident"
when
c : Customer( timeSinceJoin > 2);
not (Accident(customerid == c.name))
then
c.addDiscount(10);
end
26. For all
● True if all objects selected by first part of the
query satisfies rest of the conditions. For an
example following rule give 25 discount to
customers who has brought every type of plans
offered.
rule "OtherPlans"
when
forall ($plan : PlanCategory() c : Customer(plans
contains $plan))
then
c.addDiscount(25);
end
27. Exists
● True if at least one matches the query,
● This is Different for just having Customer(), which is
like for each which get invoked for each matching set.
● Following rule give a discount for each family where
two members having plans
rule “FamilyMembers"
when
$c : Customer()
exists (Customer( name contains $c.family))
then
c.addDiscount(5);
end
28. From
● True if at least one matches the query,
● This is Different for just having Customer(), which is
like for each which get invoked for each matching
set.
● Following rule give a discount for each family where
two members having plans
rule "validate zipcode"
when
Person( $personAddress : address )
Address( zipcode == "23920W") from $personAddress
then
# zip code is ok
end
29. Collect
● True if at least one matches the query,
● This is Different for just having Customer(), which is
like for each which get invoked for each matching set.
● Following rule give a discount for each family where
two members having plans
rule "Raise priority if system has more than 3 pending alarms"
when
$system : System()
$alarms : ArrayList( size >= 3 )
from collect( Alarm( system == $system, status == 'pending' ) )
then
# Raise priority, because system $system has
# 3 or more alarms pending. The pending alarms
# are $alarms.
end
30. Accumulate
● True if at least one matches the query,
● This is Different for just having Customer(), which is like
for each which get invoked for each matching set.
● Following rule give a discount for each family where two
members having plans
rule "Apply 10% discount to orders over US$ 100,00"
when
$order : Order()
$total : Number( doubleValue > 100 )
from accumulate( OrderItem( order == $order, $value : value ),
init( double total = 0; ),
action( total += $value; ),
reverse( total -= $value; ),
result( total ) )
then
# apply discount to $order
end
31. Conflict resolution
• Each rule may define attributes There are other parameters
you can found from [1]. E.g.
rule "MinimumAge"
salience = 10
when
c : Customer(age < 17)
then
c.reject();
end
• salience define priority of the rule and decide their activation
order.
1. http://labs.jboss.com/drools/documentation.html
32. WSO2 Deployment as an
Example
● There are our product Instances (e.g. WSAS, Greg,
IS, ESB) etc, which has been mapped to a facts in
the rule system, and it is updated using a system
management solution.
● They have a role property, that says what is there
role. e.g. ESB role=”lb” means it is a load balancer.
● Each host is represented by Host() object, and each
server have a property called host.
● Each server has a state, which is Up, Down
33. Question 1:
● A system is considered healthy if there is One ESB,
two WSAS instances, one load balance instance, one
BPS server, and one Identity server.
● Write a rule to detect if system is healthy and print it.
When
ESB(role !=”lb”);
List(size ==2) from collect (WSAS());
ESB(role==”lb”);
BPS();
IS();
then
System.out.println(“System Healthy”);
end
34. Question 2:
● If any server has failed but the host is up, restart the
server using a rule. Assume there is
system.restart(service-name, host).
When
wsas:WSAS(state=”Down”);
host:Host(state=”Up” && wsas.host=name);
then
system.restart(wsas);
end
35. Question 3:
● If a host has failed, start the servers in the host using
a rule. Assume there is system.start(service-name,
host).
When
wsas:WSAS();
host:Host(state=”Down” && wsas.host=name);
then
system.start(wsas);
end
36. Question 4:
● Assume there are cluster of 10 WSAS nodes, if the
average TPS is > 300, add a new node to the cluster.
Assume WASA expose TPS via a property called tps.
rule "Apply 10% discount to orders over US$ 100,00"
when
$order : Order()
$total : Number( doubleValue > 300)
from accumulate( WSAS( $tps : tps),
init( double tpstot = 0; int count = 0; ),
action( tpstot += $tps; count++ ),
reverse( tpstot -= $tps; count-- ),
result( tpstot/count ) )
then
# apply discount to $order
end
37. Drools Performance
• Measuring Rule engine performance is tricky.
• Main factors are number of objects and number of rules. But results
depends on nature of rules.
• A user feedback [1] claims Drools about 4 times faster than JRules [4].
(SequentialRete)
rules Objects Drools JRules
1219 100 4ms/4ms 16ms/15ms
• [2] shows a comparison between Drools, Jess [5] and Microsoft rule
engine. Overall they are comparable in performance.
1. http://blog.athico.com/2007/08/drools-vs-jrules-performance-and-future.html
2. http://geekswithblogs.net/cyoung/articles/54022.aspx
1. Jess - http://herzberg.ca.sandia.gov/jess/
2. JRules http://www.ilog.com/products/jrules/
38. Drools Performance Contd.
I have ran the well known rule engine bench mark [1] implementation provided with Drools. (On linbox3 - 1GB memory, 4 CPU
•
3.20GHz )
Bench Marks Rule Count Object Time (ms)
Count
Waltz 31 958 1582
31 3873 9030
Waltz DB 34 393 420
34 697 956
34 1001 1661
34 1305 2642
http://www.cs.utexas.edu/ftp/pub/ops5-benchmark-suite/HOW.TO.USE
1.
