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Intro to Jess The Java Expert System

Shell

Ing. Federico Cabitzafederico.cabitza-at-disco.unimib.itMAClab – http://www.mac.disco.unimib.it

Get your facts first, and then you can distort them as much as you please.- Mark Twain

To break the ice…

We’ll speak of facts and rules for a couple of hours, so… keep always in mind these two wise hints:

There are two rules for success:1) Never tell everything you know.- Roger H. Lincoln

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Agenda

1) What are expert systems (ES)?2) What are rule-based ES?3) Introduction to Jess4) The Jess Language5) Jess API & Jess Scripting6) References

…more or less 4h and 70 slides…Prepare yourself! ;-)

Expert System

An ES is a program that is able to “emulate” human reasoning and decision taking in some way and in some domain.

A very general definition:

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Expert Systems

They are quite common, embedded in everyday softwares and their use ranges from easy tasks to very difficult ones.

Some use domains:

Spam filteringFault diagnosisMedical diagnosisFactory Process ControlPrediction/classification…

Expert System

“a program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution.”

Feigenbaum 1982

A historical citation:

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An Expert System has…

some inferencing(reasoning on factual knowledge)

some interface to world (user and enviroment)

some expertise(expert knowledge in a particular area)

KNOWLEDGE BASE

INFERENTIAL ENGINE

API’s, Shell, Etc….

A very general architecture:

Expert System: inside

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Expert System: outside

expertise

solutions

users

expertsprogrammers

KNOWLEDGE BASE

INFERENTIAL ENGINE

API’s, Shell, Etc….

KNOWLEDGE BASE

INFERENTIAL ENGINE

API’s, Shell, Etc….

models,algorithms

facts

World representation

A conceptual guide

In the following we’ll treat the three keywords above outlined

Expertise: we’ll speak of Knowledge and Rules

Inferencing: we’ll speak of reasoning and the way Rule

Based ES’s do it

Interface: we’ll speak of a Java Interface to RBES’s: JESS !

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Knowledge

What is it? I mean, in this context…Three, ever present, concepts:

Mind: in Expert Systems jargon they are used quite idiosyncratically between the classical (if any) AI acceptation and the classical ICT one.Misunderstanding is no surprise…

DataInformation

Knowledge

Knowledge

In the Expert System domain:

A proceduralmodel of the world

“Factual” knowledge

Data

Information

Knowledge

inference

Facts about the world

Recommendations, solutions, etc.

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Knowledge

Data

Information

Knowledge

processing

infering

A DIKW (??) view of the same concept:

Usual Programs

Expert Systems

The DIKW Pyramid

Zeleny, M. "Management Support Systems: Towards Integrated Knowledge Management," Human Systems Management, 7(1987)1, pp. 59-70.

Ackoff, R.L. "From Data to Wisdom", Journal of Applied Systems Analysis, Volume 16, 1989 p 3-9.

In 1987 Milan Zeleny and then Russell Ackoff posited the hierarchy of Data, Information, Knowledge and… Wisdom

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By the way…

In your opinion,Information is a stuff or a process??

Knowledge examples

Your Bank Balance is:cash: 1000,00 €trading: 100,00 €

This is dataa multivalue, typed data

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Knowledge examples

Over the last month your trading made you earn 1000,00 €. Your cash has decreased by 90%

This is information.The result of some processing over data: here just an algebric sum and a proportion

Knowledge examples

Your cash is low, you should sell some titles andput some cash on your Account balance

This is what an ES could propose according to the actual situation and to some simple knowledge of bank accounts (being in the red is a real pain!! ;-)

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Knowledge examples

Hey, pal! Trading is risky and you won’t be so lucky forever!Be careful!!

This is wisdom.We don’t treat it in this course… ;-)

Types Of Expert Systems

- Neural Networks

- Blackboard Systems

- Belief (Bayesian) Networks

- Case-Based Reasoning

- Rule-Based Systems

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- represent knowledge as a bunch of rules that tell you what you should do or what you could conclude in different situations represented as a bunch of facts

- originated from AI research in the 70s and 80s.

MYCIN 1974 written in LISP by Shortliffe

Rule-Based Expert Systems

Rules in a nutshell

Rules are computational units that are computed only when they are applicable. A rule is composed of an IF-portion (LHS) and a THEN-portion (RHS).

some patternssome actions

to execute

IF THEN

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Rules in a nutshell

IF THEN

A RBES compares facts of the world with patterns of rules

..whenever the patterns of the rule match with some facts of the environment, then the rule is activated and its actions executed.

In emulating human reasoning RBES can reason (“infer”) in two ways:

- deductively (forward-chaining)

- abductively (backward-chaining).

Rule-Based Expert Systems

Both the “directions” are valid: it depends on the situation and on the required outcome which is the most suitable.

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Deduction:

Induction:

The act or process of deriving logical conclusions from premises known or assumed to be true

The process of deriving general principles from particular facts

Rule-Based Expert Systems

Abduction: The process of deriving causes from the effects

Rule-Based Expert Systems

Deduction Forward Chaining aka “production systems” :

Known facts “New” factsrules

OPS5 (1977) in LISP

1) start from initial facts 2) keep using the rules to draw new conclusions

(or take certain actions)

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Abduction Backward Chaining

PROLOG (1972) in ALGOL

1) start from some hypothesis (or goal) to prove2) keep looking for rules that would allow you to conclude that hypothesis

(also by setting new subgoals to prove as you go).

Right rules New factsKnown facts

Rule-Based Expert Systems

When to Use Rule-Based Systems

Problem Domainnarrowwell-understood

Knowledge Representation facts rules

Output recommendations to humans, support to decisions

Learning not essential

often!

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RBES Architecture

Rule Base (knowledge base)

Working Memory (fact base)

Rule Engine (inference engine)

INFERENCEENGINEWORKING

MEMORY

RULE BASE

EXECUTIONENGINE

PATTERNMATCHER

CONFLICT RESOLVER

RBES Architecture

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Inference (Rule) Engine

Pattern Matcherdecides what rules to fire and when

(conflict set).Conflict Resolver

schedules the order in which activated rules will fire (agenda).

Execution Engineresponsible for firing rules and executing other code.

Inference Process

1) Match the facts against the rules.

2) Select which rules to fire.

3) Execute the actions associated to rules.

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1) Initial facts put in a working memory (WM)

2) Rules and facts compared using pattern matching.

3) Matched rules activated put into a conflict set.

4) Conflict set resolved into agenda (ordered c.s.)(process called conflict resolution).

5) Rule engine fires on agenda (->new facts in WM).

Engine cycles until all rules in agenda are fired.

Inference Process details

Halt

Break Time

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JESSsome definitions

The Java Expert System Shell is a toolfor building Expert Systems in Java.It can be viewed also as…

…a programmer's library

…an interpreter

…a scripting language

…an interface (API’s)

well… a shell!!

Developed entirely in Sun's Java languageat Sandia National Laboratories (Livermore, California, USA)in late 1990s by Dr. Ernest J. Friedman-Hill.

JESScredits

http://herzberg.ca.sandia.gov/jess/

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• Jess matches facts in the fact base to rules in the rule base.

• The rules contain function calls that manipulate the fact base and/or other Java code.

• Jess uses the Rete (ree-tee) algorithmto match patterns.

• Rete network = an interconnected collection of nodes = working memory.

JESSin short

The Jess Language

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Syntax similar to LISP(Lots of Irrititating, Superflous Parentheses)

Basic data structure is the list.

Inspired to CLIPS (C Language Integrated Production System)

all structured data are lists:

facts, rules, also function calls are lists.

The Jess Language

Numbers: INTEGER, LONG, FLOAT

Data Types

Atoms: ATOM

foobarFoobarnumber#3 3#number

Nil TRUE FALSE

0.357 42 6.0E4

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Data Types

Strings: STRING

“0.357” “foobar” “ \”between commas\” ”

( foobar) (a foobar 4) (deftamplate foo (slot bar))

Lists: LIST

head

Comments: ;*

Obligatory Tradition

Your very first Jess program!

(printout t “Hello World!” crlf)

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Functions in Jess

Function call: a list whose head is the name of an existing function list

Prefix notations

Nesting: the outer function evaluates the inner one(s)

(+ 2 3) 5

(* (+ 2 3) (+ (* 2 3) 2))

5 8 640

(printout t “Hello World!” crlf)

• Atoms that begin with a ? mark.

• Untyped: types can change during lifetime (like in dynamically typed languages)

• Can be local or global (aka defglobals)

• Variables (?x) are declared by first assigning(using bind function), defglobals (?*x*) areassigned in declaration

Variables in Jess

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(bind ?x 2)

(defglobal ?*y* = 3)

(bind ?result (+ ?x ?y))

Variables in Jess

assignes value 2 to variable x

assignes value 3 to global variable y

assignes to variable resultthe result of the expression(3 + 2)

An example

Variables in Jess

Differences between variables and defglobals??

Easy:Global variables (defined through the defglobalconstruct, hence defglobals) are variables thatare not wiped out by issuing the resetcommand.

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Functions in Jess

You can create a function by creating a particular list whose head is the deffunction atom

(deffunction max (?a ?b)(if (> ?a ?b) then

(return ?a)else

(return ?b))

)

Functions in Jess

In a function you can create any variableand use it with input parameters. Example:

(deffunction area-circle (?radius)(bind ?area (* (pi) (* ?radius

?radius)))(return ?area))

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Functions in Jess

Putting things togetherafter having created a new function, touse it is quite trivial. Example:

(printout t "The area of a 2 meter radius circle is " + (area-circle 2) + " m^2")

Common Functions

(reset): resets the Working Memory *(clear): clears Jess (more deeply than reset)(facts): shows facts currently in WM(rules): shows rules currently in WM(batch <filename>): sends the Shell some file content(run): starts the engine(apply) calls a function on a given set of arguments.(build) parses and executes a function from a string.(eval) ”(open <filename> id w): opens a file in write mode(read t): reads from Standard Input(read id): reads the file content(printout t “…” crlf): prints to StdOutput(printout id “…” crlf): prints to file(exit): exits the Shell

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Let’s talk facts

Jess (and any other RBES) representsthe world and simple knowledge of it bymeans of facts.

Facts are lists with a specific structure:

( atom [slot]*)headlike a label, it expresses the category of the fact

slotsData containers (simple or lists…)

Jess Facts Types

- Ordered, not structured

Where just position counts.

- Unordered, structured

Where types and default values are allowed.

- Shadow

Where Jess and Java meet.

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Jess Facts Examples

(person (surname “Smith”)(first-names Robert Jerome) (age 35)

)

Slots: like fields of a form, they hold (un)typed data.(first-names is a multislot: see below)

(error-occurred)

(shopping-list eggs milk bread)

(person “Robert J. Smith" Male 35)

Just a head, no slot: this fact indicates a state in the world

Jess Facts assertion

Facts “exist” if they are asserted.Asserting a fact means to put it in the “fact base”.Likewise, retracting a fact means to remove it from the Working Memory.

Jess> (assert (shopping-list eggs bread))<Fact-1>

Jess> (facts)f-0 (MAIN::initial-fact)f-1 (MAIN::shopping-list eggs bread)For a total of 2 facts.

Jess> (retract (fact-id 1))

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Deftemplate

To work with unordered facts they must be defined first by calling the deftemplateconstruct.

(deftemplate <deftemplate-name>(slot | multislot <slot-name>)* )

A multifield slot (multislot) can have any number (including zero) of values.

Deftemplate Ex. 1/2

Jess> (deftemplate automobile"A specific car."(slot make)(slot model)(slot year (type INTEGER))(slot color (default white))

)

Jess> (bind ?id (assert (automobile (make Volvo) (model 460)(year 1997))))<Fact-0>

Jess> (facts)f-0

(MAIN::automobile (make Volvo) (model 460)(year 1997) (color white))

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Deftemplate Ex. 2/2

Jess> (facts)f-0

(MAIN::automobile (make Volvo) (model 460)(year 1997) (color black))

Jess> (modify ?id (color black))

Modify will… just modify a value in a slot!

Shadow Facts

- Defclasscreates a deftemplate that represents a

category of Java bean.

- Definstanceadds a representation of one specific bean to

working memory.

Shadow facts are unordered facts whose slots correspond to the properties of a JavaBean.

Sort of class import

Sort of object instance

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What are patterns?How actions

are expressed?

IF THEN

Jess Rules…

Now we can look inside these boxes…

This is the rule syntax:

(defrule <rule-name> {;optional comment}<condition-element-1><condition-element-2>…..<condition-element-k>

=><action-1>…..<action-m>

)

Jess Rules Syntax

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What is a Condition Element? (CE)

<condition-element> =<pattern> or <NOT-CE> or <AND-CE>

or <OR-CE> or <test-CE>

Condition Elements

Yes, this is a recursive definition……at last CE’s are bunches of patterns and some of them can be evaluated in logical expressions.A CE can be either True or False.

Eg.: an OR-CE is true if any CE is True, False otherwise.

When is a CE true?

CE and Patterns

It depends on whether the patterns that constitute it find a match in the WM.

Patterns are partial descriptions of data.In Jess they can be expressed by facts that include some variables looking for a… value!

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(Exp (Name ?X) (Arg1 5) (Op ?))

(Exp (Name E21) (Arg1 5) (Op *) (Arg2 X))

… will match with this fact:

1) This pattern…

3 Patterns Examples

field constraintliteral constraint

wildcard constraint

2) ? is a monofield wildcard; $? is a multifield one.Ex.: $?items can match any number of values

Patterns Examples

(defrule cart-containing-milk(shopping-cart (contents $?anything_before milk $?anything_after))

=>(printout t “The cart contains milk.” crlf))

3) You can use Connective Constraints (&, |, ~, :)

?x&:(> 5 ?x) match with any x that is less than 5

(person (city ~Milano& ~Roma)) match with anyone who lives neither in Rome nor in Milan

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Patterns and rules…

If all the LHS CE’s of a rule are True thenthe rule instance is entered into the conflict set and becomes an Activation.Then attention moves to the RHS, where actions wait to be fired. Actions are expressed quite easily…they are function calls!!

;; This is a trivial error handler

(defrule report-error

(error-is-present)

=>

(printout t “There is an error” crlf))

Let’s see again the JESS rule syntax in a simple example:

definition

L H S

then R H S

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What a Rule can do…

A rule can change the Fact base

Adding…(assert ()) to assert a fact(deffacts ()) to assert more unstructured facts

Removing…(retract (fact-id ?fact-id)) to delete a fact

Modifying…(modify ?f (slotname slotvalue))

to modify a fact

( defrule fratelli_mamma(madre ?mamma ?figlio1)(madre ?mamma ?figlio2)(test (neq ?figlio1 ?figlio2))=>(assert (fratelli ?figlio1 ?figlio2))

)

IF a mother has got two children…

… I mean, TWO children

THEN

These two guys are brothers!!

Jess Rule Example

Not a big deal, uh?! ;-)

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A More Complex Rule

;; A better error handler

(defrule report-err?err <- (is-error (msg ?msg))=>(printout t "Error was: " ?msg crlf)(retract ?err))

A more useful rule…

Inference Process details

1) Initial facts put in a working memory (WM)

2) Rules and facts compared using pattern matching.

3) Matched rules activated put into a conflict set.

4) Conflict set resolved into agenda(process called conflict resolution).

5) Rule engine fires on agenda (new facts in WM).

Engine cycles until all rules in agenda are fired.

Halt

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Conflict Resolution

The conflict set is the set of all the rules that are eligible to be fired (executed).

Conflict resolution is the process of putting the rules in firing order.

Different strategies can be applied according to the domain (and how this is formalized into rules).

Usually strategies are accomplished according to some rationale (trade-off).

Conflict Resolution

- Rule-based rationale- Recency (activation order) - Specificity (number of conditions, or lenght of conditions)- Anti-refraction (not the same rule under the same conditions) - Anti-repetition (not the same effect twice)- …

1. - Object-based rationale- Classification and ranking of object referenced in LHSs

2. - Effect-based rationale- Assessment of effects beforehand

3. - Metarules

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Conflict Resolution

The Jess built-in strategies are depth and breadth and can be called with the (set-strategy (depth | breadth)) command.

- Depth (default):The most recently activated rules fire first. Last in, first out.

Caveat: troubles arise if every fired rule activates another rule, since old activations can never get a chance to fire.

Conflict Resolution

- Breadth: The most recently activated rules fire last (i.e., firing order is activation order): First in, first out.

Not enough? You can write your own strategy in Java by implementing the jess.Strategyinterface (‘compare’ method):

(set-strategy your-alluring-strategy)

You lazy? You can use salience…

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Salience

- Each rule has a property called salience.It’s sort of rule priority.

- Activated rules of the highest salience will fire first, followed by rules of lower salience.

- Rules can include a salience declaration

(defrule last-rule(declare (salience -100))(command exit-when-idle)=>(printout t "exiting..." crlf))

Java ← Jess

- Jess comes with other 200 built-in functions and you can also define any function you want. This makes Jess a good scripting language.

- More importantly from JESS you can access all Java APIs (link to databases, GUI forms, etc.), create and manipulate Java objects.

(foreach <variable> <multifield-expression> <action>*)

(if <expression> then <action>* [else <action>*])

(try <expression>* [catch <expression>*])

(while <expression> [do] <action>*)

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Jess> (bind ?ht (new java.util.Hashtable))<External-Address:java.util.Hashtable>

Jess> (call ?ht put "key1" "element1")Jess> (call ?ht put "key2" "element2")Jess> (call ?ht get "key1")

"element1"

Examples

Name of an existing class

Methods of the class

Examples

public class java_class{public int fibonacci(int n){

if(n < 3) return 1;else return fibonacci(n-1) +

fibonacci(n-2);}

}

Jess> (defglobal ?*external-class-obj* = 0)Jess> (bind ?*external-class-obj* (new java_class))) Jess> (printout t "The 6th fibonacci number is: “

(?*external-class-obj* fibonacci 6) crlf)

This class must be compiled

Methods of the class

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Java → Jess

From Java you can call JESS (Jess API).

import jess.*;

public class ExSquare { public static void main(String[] str) { try {

Rete r = new Rete(); r.executeCommand(“(deffunction square (?n)

(return (* ?n ?n)))"); Value v = r.executeCommand("(square 3)"); System.out.println(v.intValue(r.getGlobalContext()));

} catch (JessException ex) { System.err.println(ex);

} }

}

try {

Rete engine = new Rete();

engine.executeCommand(

(batch foobar.clp));

engine.executeCommand(“(run)”);

}

catch (JessException je) {

System.err.println(je);

}

Java → Jess

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Two good methods to exchange data between Java and Jess and viceversa.

Java → Jess

- Using Fetch / Store commands

- Using Shadow facts

Shadow Facts

- Defclasscreates a deftemplate that represents a

category of Java bean.- Definstance

adds a representation of one specific bean to working memory.

Shadow facts are unordered facts whose slots correspond to the properties of a JavaBean.

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Java → Jess

Shadow factsPublic class JavaBean{public String getPropertyOne() …public void setPropertyOne(String)…public int String getPropertyTwo() …public void setPropertyTwo(int)…

}

(deftemplate Javabean(slot propertyOne)(slot propertyTtwo)

)

Java → Jess

public class DimmerSwitch { private int brightness = 0; public int getBrightness() { return brightness; } public void setBrightness(int b) {

brightness = b; adjustTriac(b);

} private void adjustTriac(int brightness) {

// Code not shown }

}

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Java → Jess

Jess> (defclass dimmer DimmerSwitch)

This generate a deftemplate that represent the class

Jess> (bind ?ds (new DimmerSwitch))

This creates an instance (object) of DimmerSwitch (it’s a Java Object)

Jess> (definstance dimmer ?ds)

This adds the object into the working memory

Jess> (call ?ds setBrightness 10)

This is a call to a method of Dimmerswitch classthat changes the attribute of the object referenced by ?ds.

Java → Jess

If the definstance is static only the object changes, not the fact (unless you run a reset command). In fact the reset updates the shadow fact to match the JavaBean, like (update ?ds).

In the “dynamic” fact Jess puts a PropertyChangeListener but to have a sf change when a bean does, you need also to define a PropertyChangeSupport in your java class.

Jess> (definstance dimmer ?ds [static])

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Java → Jess

A propertyChangeSupport

private PropertyChangeSupport pcs = new PropertyChangeSupport(this);public voidaddPropertyChangeListener(PropertyChangeListener p) {

pcs.addPropertyChangeListener(p);}public void

removePropertyChangeListener(PropertyChangeListener p) {pcs.removePropertyChangeListener(p);

}

1) Add these code in the class definition

2) Change the Set Method of the JavaBean

In the setBrightness method of the example: pcs.firePropertyChange(“brightness",new Integer(old),(new Integer(b)));

Java → Jess

Jess> (facts)f-0 (MAIN: :dimmer

(brightness 0)(class <External-Address…>)(OBJECT <External-Address…>)

)

(defrule example(dimmer (OBJECT ?o))(test (eq (call ?o getBrightness) 10))

=>(assert (Brightness 10))

)

How to use a shadow fact in a rule…

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Java → Jess

You CANNOT use assert on shadow facts (sf).Use definstance instead.Neither you can assert a sf with deffactsconstruct.

You CAN use both retract and modify on sf.If you modify a sf, Jess automatically updates the corresponding JavaBean property.

(call ?sf setProperty x) == (modify (Property x))

Java → Jess

At lab we’ll see inside the black box called foobar.clp!!

engine.executeCommand((batch foobar.clp));

See you there!!

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Some labs tricks1) Independent engines: Note that each individual jess.Rete object represents an independent reasoning engine. A single program can then include several independent engines. Jess can be used in a multithreaded environment.

2) Mind that try-catch in the rete thread is blocking

3) Conceive any fact in jess as a javabean with some properties. At the end of the execution go and read the properties to check what has been modified by inferential engine.

4) Initialize the javabeans, make in the jess code the proper links (“shadows”) to these beans and then let the program reason for you by reading and writing on these javabeans (as well as on other java objects)

5) Mind that scripts MUST be well-written (clear, defclass, definstances, deftemplate, deffacts, defrule, reset…)

Expert Systems References

- Friedman-Hill, E. J., Jess In Action, Manning Press, 2003 (strongly recommended)

- Jackson, P., Introduction to Expert Systems –3rd Ed., Addison-Wesley, 1999

- Giarratano, J., Expert Systems: Principals and Programming, PSW-Kent, 1989

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Jess Links

- Download Jess at: http://herzberg.ca.sandia.gov/jess/index.shtml

- The official Jess Manual by E.J. Friedman-Hillhttp://herzberg.ca.sandia.gov/jess/docs/manual.pdf

- A Jess presentation by Jason Morrishttp://www.pjug.org/introtojess.ppt

- A Jess tutorial by Maarten Menken http://www.cs.vu.nl/~ksprac/export/jess-tutorial.pdf

Q & A

Thanks for your attention!

Last modified April 2005An updated version of these slides will be

regularly uploaded at http://www.entechne.comPlease, report any mistake to author. Thank you.