Master the RETE algorithm

Master the RETE algorithm!!
– The Heart of Rule Engine -
Red Hat K.K.
Chief Technologist
Masahiko Umeno
Red Hat Forum Tokyo 2018
AP09

About Rule Engine
Copyright © 2018 Red Hat K.K. All Rights Reserved.2
A converter that triggers new actions due to the relevance of different
types of data
Example…
Preferred
Channel
For those who buy a lot of
strawberry flavor.
At the time you are browsing.
Send Ad with those preferred
channel.
purchase
history
12:13
Browsing
history

Agenda
RETE
Algorithm
Basic
Behavior
Apply to
Machine
Learning

RETE Algorithm
Thought to realize a production system,
Efficient Pattern Matching
Production System: Planning, Expert system, Action selection
KnowledgeData Action

History of RETE Algorithm
Mycin
Dendral
1972
Started from the expert system
Prolog
RETE
1974
OPS5
CLIPS
Jess
DroolsSoar
ILOG
Rules
1984
1983
1995
2001
1987
JBoss Enterprise
BRMS 5
2009
Today
1996
Red Hat
JBoss
BRMS 6
Revelation
RETE

Structure
Working Memory Production Memory
Agenda
Data (FACT)
The place of Action candidate which matched data and rule
Knowledge

Matching ofTruck and Driver
Rule
• Fix a truck when broken truck and parts are existing
• Assign driver to truck when empty truck and available
driver are existing
Please consider what flow may require using Rule Engine.
If not use Rule Engine?
All pictures drawn by Nagisa.

Step 1/12
Working Memory Production Memory
Agenda
Condition
Action
Condition
Action

Step 2/12
Working Memory
Condition
Action
Condition
Action
Production Memory
Agenda Condition
Action

Step 3/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 4/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 5/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 6/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 7/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 8/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 9/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 10/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 11/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Step 12/12
Working Memory
Agenda
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Condition
Action
Production Memory

Data Mode (Fact)
Copyright © 2012 Red Hat, Inc.
public class Truck {
private String Color;
private String Parts;
private String Driver;
}
public class Parts {
private String Parts;
}
public class Driver {
private String Name;
}
21
Create class and members
taking into account what the
truck attribute values are.

Rule
Copyright © 2012 Red Hat, Inc.
rule "Repear"
salience 100
when
p : Parts ()
t : Truck (parts == p.parts )
then
t.setParts ("Complete");
retract (p);
update (t);
end
rule "Matching"
salience 10
when
t : Truck (parts == "Complete", driver == "")
d : Driver (n : name )
then
t.setDriver (n);
retract (d);
update (t);
end
22
Update (x);
Notice to Rule Engine when Fact(x)
changed. Rule Engine may reevaluate
using new status of Fact, create
Activation in Agenda when data matched
rule.
Retract (x);
Delete Fact(x) from Working Memory.
Cancel Activation from Agenda when
relevant activations are existing.

Audit Check the behavior when rule fired
Fact Update
Activation
Created Activation at
matched rule and Fact when
Fact inserted.
Canceled Activation at un-
matched rule when Fact had
remove.
Copyright © 2012 Red Hat, Inc.23

Characteristic of RETE Algorithm
• Engine that causes Action based on the relevant
between objects (Fact and rule, Fact and Fact and
rule, etc.)
• When Fact is updated and notified to the rule
engine, it is re-evaluated and executed sequentially
with a combination of matching Facts and rules.
• Weak to combination explosion
• Can not detect infinity loop
– Enable to avoid with the number of fired rules.

PHREAK
• (Marc) Proctor’s Hybrid Reasoning Engine for
Advanced Knowledge
– Improved RETEOO(RETE Object Oriented)
• Lazy evaluation
– Rather than evaluating at once, prevent
combination explosion in Agenda (when the
condition is multistage)
• Faster
– Multithreading for evaluation

Effective use of RETE Algorithm to Business
Feature Notes
Correlation
Check
Business Screening, Assessment, Derivation, Quality check of Data in DB
System Hierarchy, Correlation check of distributed data
Aggregation Business Agency Incentive, Cost item, Simulation
System Hierarchy, Correlation check of distributed data
Reasoning Business Production Control, Production Planning, Routing, Assign
System Forward Chaining, Backward Chaining, Truth Maintenance System

Summary of Red Hat Decision Manager Rule Engine
• Evaluate rule based on Data
• Fire continuously by the reasoning mechanism, and
re-evaluate. Simplify the logic for complex.
• Because of tracing of behavior can available, it
makes not be a black-box.
• PHREAK , it's an improved RETE algorithm is
included.
• Not a simple if-then logic engine!!

Leaning
Supervised
Learning
Unsupervised
Learning
Human wisdom
Business Rule
Supervised Learning
• Neural Network
• Regression
• Tree
• Bayesian inference
• Clustering k-nearest
neighbor
Unsupervised Learning
• Principal component
analysis
• Clustering k-means
• Vector Quantization
• Self Organizing map

Clustering
• One of the machine learning
• Primitive, but important methodology
• Used to Data mining
• Outcome is “Case divider”
• Use the outcome to “supervised learning”/“Rule”
• Non-hierarchical: k means
• Hierarchical: Single Linkage Method, Furthest
neighbor method, Group average method…

Single Linkage Method
1. Calc the distance between any two objects
2. Of the two arbitrary objects, the object between the
smallest distances is extracted, and the center of
gravity is newly created as an object. Record the
relationship between the new object and the two
objects.
3. An object cannot belong to more than one group, so if
it belongs to a different group, give up that it belongs
to one of the groups
4. Loop 1-4 until the number of parent objects is less
than the number what we want to.

1 DIMENSION
Let’s cluster with Single Linkage Method using Rule Engine !!

Data
7 8 18 27 37.5 62 72 73 89 96
• Value 0 - 100
• Number of points is 10 (Duplicateable)
• Random plot
3 Clustering

Final Result
7.5
92.526.25 67.25
15
37.5
22.5
62
72.5
18 27 89 967 8 72 73

Data Model (Fact)
public class Distance {
private double distance;
private AnalysisData a1;
private AnalysisData a2; }
Distance between 2 points
public class AnalysisData {
private double x;
private double y;
private String Tag="";
private int Color=0;
private int level=0;
private ArrayList<AnalysisData> subsidiary;}
Data of clustering
Coordinate and flags,
The list of data included

Rules
rule "Calc distance between 2 points"
rule “Get shortest distance and crate new center of gravity”
rule “Remove duplicated child from subsidiary”
rule “Calc the number of cluster, delete old distance”
rule “Color to the most upper layer”
rule “Colored to the other same as the most upper layer "
Clustering
Coloring

rule "Calc distance between 2 points"
salience 100
when
ad1: AnalysisData(x1:x , y1:y, tag =="")
ad2: AnalysisData(x2:x >=x1 , y2:y, tag =="", this != ad1)
not Distance(a1==ad1, a2==ad2)
not Distance(a1==ad2, a2==ad1)
then
Distance d = new Distance();
d.setDistance(Math.sqrt(Math.pow((x1-x2),2)+Math.pow((y1-y2),2)));
d.setA1(ad1);
d.setA2(ad2);
insert (d);
end
tag: set child when became child of someone
Calculation as Euclidean distance

rule “Get shortest distance and crate new center of gravity "
salience 100
when
d1 : Distance( minDist:distance )
not Distance( distance < minDist)
A1 : AnalysisData( tag =="") from d1.getA1
A2 : AnalysisData( tag =="") from d1.getA2
NumberOfCluster( number > CulsterNumber )
then
AnalysisData ad = new AnalysisData();
ad.setX((d1.getA1().getX()+d1.getA2().getX())/2);
ad.setY((d1.getA1().getY()+d1.getA2().getY())/2);
ad.setTag("");
ad.setLevel(1);
ad.addSubsidiary(d1.getA1());
ad.addSubsidiary(d1.getA2());
insert(ad);
A1.setTag(“Child”); update(A1);
A2.setTag(“Child”); update(A2);
retract(d1);
end
tag: Set Child when it become a child
There is nothing less than my distance
= Minimize myself
Level: Hierarchies (init 0)
Create new center
of gravity
(Use same class as data)
Notify new center of gravity created to Rule

rule " Remove duplicated child from subsidiary "
salience 200
when
ad1: AnalysisData( )
ad2: AnalysisData( tag =="", subsidiary contains ad1)
ad3: AnalysisData( this != ad2, subsidiary contains ad1)
ad4: AnalysisData( this != ad1 ) from ad3.getSubsidiary()
ad5: AnalysisData( this != ad1, this != ad4) from
ad2.getSubsidiary()
then
ad4.setTag("");
retract (ad3);
update(ad4);
end

rule “Calc the number of cluster, delete old distance "
salience -100
when
total: Number(intValue >= CulsterNumber.intValue())
from accumulate (AD: AnalysisData( tag != "Child"), count(AD))
noc: NumberOfCluster()
d: Distance()
then
noc.setNumber(total.intValue());
update (noc);
retract (d);
end
tag != "Child” stands for the parent node of a cluster
The number which want to divide
C
CC C

Step 1
7 8 18 27 37.5 62 72 73 89 96

Step 1
7 8 18 27 37.5 62 72 73 89 96
1 10 9 11 24 10 1 16 7
7.5 72.518 27 37.5 62 89 96
The # cluster : 8

Step 2
18 27 37.5 62 89 967.5 72.5
11.5 24 10.5 16.5 79 11
92.518 27 37.5 62 72.57.5
The # cluster : 7

Step 3
18 27 37.5 627.5 72.5 92.5
11.5 24 10.59 11 20
37.5 62 72.57.5 92.522.5
The # cluster : 6

Step 4
37.5 627.5 72.5 92.5
15 24 10.515 20
22.5
37.57.5 92.522.5 67.25
The # cluster : 5

Step5
37.57.5 92.5
15
15 29.7515 25.25
22.5
30
67.25
92.567.25
The # cluster : 4

rule "Remove duplicated child from subsidiary"
salience 200
when
ad2: AnalysisData( tag =="", subsidiary contains ad1)
ad3: AnalysisData( this != ad2, subsidiary contains ad1)
ad4: AnalysisData( this != ad1 ) from ad3.getSubsidiary()
ad5: AnalysisData( this != ad1, this != ad4) from ad2.getSubsidiary ()
then
ad4.setTag("");
retract (ad3);
update(ad4);
end
37.57.5
15 15
15
22.5
30
ad4
ad2
ad1
ad3
ad5

rule "Remove duplicated child from subsidiary"
salience 200
when
ad2: AnalysisData(this != ad1)
ad3: AnalysisData(this != ad1, this != ad2)
ad4: AnalysisData(tag =="", subsidiary contains ad1, subsidiary contains ad2)
ad5: AnalysisData(tag =="", this != ad4, subsidiary contains ad2, subsidiary
contains ad3)
then
ad3.setTag("");
update(ad3);
retract (ad5);
end
37.57.5
15 15
15
22.5
30
ad3
ad4
ad2
ad5
ad1
Bad Code!
500C3 = 20.70M
Can not reduce the number
of combinations
Combination Explosion occurred

Step5
37.57.5 92.5
15 29.7515 25.25
22.5 67.25
92.567.2515 30
The # cluster : 4
307.5 22.5

Step6
92.5
26.25
22.5 29.75 25.25
67.25
92.567.25
The # cluster : 3
15 37.5

Step7 Hierarchical display
7.5
92.526.25 67.25
15
37.5
22.5
62
72.5
18 27 89 967 8 72 73

k-means clustering
1. Label data to clustering randomly
2. Calc each center of gravity
3. Calc distance between data and Center of gravity,
relabel same as neighborhood
4. Loop 2-3 until Center of Gravity does not move

k-means clustering
7 8 18 27 37.5 62 72 73 89 96
50.5
39.5 56.3
7 8 18 27 37.5 62 72 73 89 96
19.5 50.5 78.4
7 8 18 27 37.5 62 72 73 89 96
15.0 49.75 82.5
7 8 18 27 37.5 62 72 73 89 96

Result
7 8 18 27 37.5 62 72 73 89 96
k-means clustering
7 8 18 27 37.5 62 72 73 89 96
Single Linkage Method
Guess:
Which is the same as human sense?

Original
• 0 - 10,000value for x/y
• The number of data is 500
• Random plot
Let’s cluster this with use same rule as aforementioned.

3 Clusters

5 Clusters

10 Clusters

15 Clusters

WRAP UP
Master the RETE algorithm!!
– The Heart of Rule Engine -

Conclusion
• Not a simple if-then logic engine!!
• Fire continuously by the reasoning mechanism, and
re-evaluate. Simplify the logic for complex.
• Need to write rule with the object combination in
mind.
• Enable to use Machine Learning!!

Other Decision Manager Sessions

17:35 Ideathon Awards
https://jp-redhat.com/forum-ideathon/

Thank you
plus.google.com/+RedHat
linkedin.com/company/red-hat
youtube.com/user/RedHatVideos
facebook.com/redhatinc
twitter.com/RedHatNews
Masahiko Umeno: mumeno@redhat.com

Master the RETE algorithm

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