1. Introduction to Jess

2. Fields
To build a knowledge base, Jess must read input from keyboard / files to execute commands and load programs.
During the execution process, Jess groups symbols together into tokens – groups of characters that have the same meaning.
A field is a special type of token of which there are 8 types.
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3. Numeric Fields
The floats and integers make up the numeric fields – simply numbers.
Integers have only a sign and digits.
Floats have a decimal and possibly “e” for scientific notation.
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4. Symbol Fields
Symbols begin with printable ASCII characters followed by zero or more characters, followed by a delimiter.
Jess is case sensitive.
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5. String Fields Strings must begin and end with double quotation marks.
Spaces w/in the string are significant.
The actual delimiter symbols can be included in a string by preceding the character with a backslash.
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6. Address Fields
External addresses represent the address of an external data structure returned by a user-defined function.
Fact address fields are used to refer to a specific fact.
Instance Name / Address field – instances are similar to facts addresses but refer to the instance rather than a fact.
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7. Entering / Exiting Jess
The Jess prompt is: Jess>
This is the type-level mode where commands can be entered.
To exit Jess, one types: Jess> (exit) 
Jess will accept input from the user / evaluate it / return an appropriate response:
Jess> (+ 3 4)   value 7 would be returned.
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8. Basic CLIPS Commands (1)
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9. Basic CLIPS Commands (2)
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10. Facts and Jess
To solve a problem, Jess must have data or information with which to reason.
Each chunk of information is called a fact.
Facts consist of:
Relation name (symbolic field)
Zero or more slots w/associated values
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11. Example Fact in Jess
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12. Deftemplate
Before facts can be constructed, Jess must be informed of the list of valid slots for a given relation name.
A deftemplate is used to describe groups of facts sharing the same relation name and contain common information.
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13. Deftemplate General Format
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14. Deftemplate vs. Ordered Facts
Facts with a relation name defined using deftemplate are called deftemplate facts.
Facts with a relation name that does not have a corresponding deftemplate are called ordered facts – have a single implied multifield slot for storing all the values of the relation name.
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15. Adding Facts Jess store all facts known to it in a fact list.
To add a fact to the list, we use the assert command.
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16. Displaying Facts Jess> (facts) 
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17. Removing Facts Just as facts can be added, they can also be removed.
Removing facts results in gaps in the fact identifier list.
To remove a fact:
Jess> (retract 2) 
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18. Modifying Facts
Slot values of deftemplate facts can be modified using the modify command:
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19. Results of Modification
A new fact index is generated because when a fact is modified:
The original fact is retracted
The modified fact is asserted
The duplicate command is similar to the modify command, except it does not retract the original fact.
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20. Watch Command The watch command is useful for debugging purposes.
If facts are “watched”, Jess will automatically print a message indicating an update has been made to the fact list whenever either of the following has been made:
Assertion
Retraction
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21. Deffacts Construct
The deffacts construct can be used to assert a group of facts.
Groups of facts representing knowledge can be defined as follows:
(deffacts <deffacts name> [<optional] comment]
<facts> * )
The reset command is used to assert the facts in a deffacts statement.
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22. The Components of a Rule
To accomplish work, an expert system must have rules as well as facts.
Rules can be typed into Jess (or loaded from a file).
Consider the pseudocode for a possible rule:
IF the emergency is a fire
THEN the response is to activate the sprinkler system
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23. Rule Components
First, we need to create the deftemplate for the types of facts:
(deftemplate emergency (slot type))
-- type would be fire, flood, etc.
Similarly, we must create the deftemplate for the types of responses:
(deftemplate response (slot action))
-- action would be “activate the sprinkler”
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24. Rule Components The rule would be shown as follows:
(defrule fire-emergency “An example rule”
(emergency (type fire))
=>
(assert (response
(action activate-sprinkler-system))))
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25. Analysis of the Rule The header of the rule consists of three parts:
Keyword defrule
Name of the rule – fire-emergency
Optional comment string – “An example rule”
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26. Analysis of Rule
If all the patterns of a rule match facts, the rule is activated and put on the agenda.
The agenda is a collection of activated rules.
The arrow => represents the beginning of the THEN part of the IF-THEN rule.
The last part of the rule is the list of actions that will execute when the rule fires.
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27. The Agenda and Execution
To run the Jess program, use the run command:
Jess> (run [<limit>])
-- the optional argument <limit> is the maximum number of rules to be fired – if omitted, rules will fire until the agenda is empty.
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28. Execution
When the program runs, the rule with the highest salience on the agenda is fired.
Rules become activated whenever all the patterns of the rule are matched by facts.
The reset command is the key method for starting or restarting .
Facts asserted by a reset satisfy the patterns of one or more rules and place activation of these rules on the agenda.
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29. What is on the Agenda?
To display the rules on the agenda, use the agenda command:
Jess> (agenda) 
Refraction is the property that rules will not fire more than once for a specific set of facts.
The refresh command can be used to make a rule fire again by placing all activations that have already fired for a rule back on the agenda.
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30. Command for Manipulating Constructs
The list-deftemplates displays the current list of deftemplates.
(rules)/(facts) display the current list of rules/facts accordingly.
The ppdefrule, ppdeftemplate and ppdeffacts commands display the text representations of a defrule, deftemplate, and a deffact, respectively.
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31. Commands
The undefrule, undeftemplate, and undeffacts commands are used to delete a defrule, a deftemplate, and a deffact, respectively.
The clear command clears the Jess environment and adds the initialfact-defacts to the Jess environment.
The printout command can also be used to print information.
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32. Commenting and Variables
Comments – provide a good way to document programs to explain what constructs are doing.
Variables – store values, syntax requires preceding with a question mark (i.e: ?example)
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33. Fact Addresses, Single-Field Wildcards, and Multifield Variables
A variable can be bound to a fact address of a fact matching a particular pattern on the LHS of a rule by using the pattern binding operator “<-”.
Single-field wildcards can be used in place of variables when the field to be matched against can be anything and its value is not needed later in the LHS or RHS of the rule (?).
Multifield variables and wildcards allow matching against more than one field in a pattern ($?)
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34. Field Constraints
In addition to pattern matching capabilities and variable bindings, Jess has more powerful pattern matching operators.
Consider writing a rule for all people who do not have brown hair:
We could write a rule for every type of hair color that is not brown.
This involves testing the condition in a roundabout manner – tedious, but effective.
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35. Field Constraints
The technique for writing a rule for all non- brown hair colors implies that we have the ability to supply all hair colors – virtually impossible.
An alternative is to use a field constraint to restrict the values a field may have on the LHS – the THEN part of the rule.
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36. Connective Constraints
Connective constraints are used to connect variables and other constraints.
Not connective – the ~ acts on the one constraint or variable that immediately follows it.
Or constraint – the symbol | is used to allow one or more possible values to match a field or a pattern.
And constraint – the symbol & is useful with binding instances of variables and on conjunction with the not constraint.
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37. Combining Field Constraints
Field constraints can be used together with variables and other literals to provide powerful pattern matching capabilities.
Example #1: ?eyes1&blue|green
This constraint binds the person’s eye color to the variable, ?eyes1 if the eye color of the fact being matched is either blue or green.
Example #2: ?hair1&~black
This constraint binds the variable ?hair1 if the hair color of the fact being matched is not black.
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38. Functions and Expressions
Jess has the capability to perform calculations.
The math functions in Jess are primarily used for modifying numbers that are used to make inferences by the application program.
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39. Numeric Expressions in Jess
Numeric expressions are written in Jess in LISP- style – using prefix form – the operator symbol goes before the operands to which it pertains.
Example #1:
5 + 8 (infix form)  (prefix form)
Example #2:
(infix) (y2 – y1) / (x2 – x1) > 0
(prefix) (> ( / ( - y2 y1 ) (- x2 x1 ) ) 0)
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40. Return Values
Most functions (addition) have a return value that can be an integer, float, symbol, string, or multivalued value.
Some functions (facts, agenda commands) have no return values – just side effects.
Division results are usually rounded off.
Return values for +, -, and * will be integer if all arguments are integer, but if at least one value is floating point, the value returned will be float.
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41. Variable Numbers of Arguments
Many Jess functions accept variable numbers of arguments.
Example:
Jess> ( )  returns = = -9
There is no built-in arithmetic precedence in Jess – everything is evaluated from left to right.
To compensate for this, precedence must be explicitly written.
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42. Embedding Expressions
Expressions may be freely embedded within other expressions:
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43. Summing Values Using Rules
Suppose you wanted to sum the areas of a group of rectangles.
The heights and widths of the rectangles can be specified using the deftemplate:
(deftemplate rectangle (slot height) (slot width))
The sum of the rectangle areas could be specified using an ordered fact such as:
(sum 20)
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44. Summing Values A deffacts containing sample information is:
(deffacts initial-information
(rectangle (height 10) (width 6))
(rectangle (height 7) (width 5)
(rectangle (height 6) (width 8))
(rectangle (height 2) (width 5))
(sum 0))
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45. Summing Values
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46. The Bind Function
Sometimes it is advantageous to store a value in a temporary variable to avoid recalculation.
The bind function can be used to bind the value of a variable to the value of an expression using the following syntax:
(bind <variable> <value>)
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47. I/O Functions
When a Jess program requires input from the user of a program, a read function can be used to provide input from the keyboard:
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48. Read Function from Keyboard
The read function can only input a single field at a time.
Characters entered after the first field up to the  are discarded.
To input, say a first and last name, they must be delimited with quotes, “xxx xxx”.
Data must be followed by a carriage return to be read.
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49. I/O from/to Files Input can also come from external files.
Output can be directed to external files.
Before a file can be accessed, it must be opened using the open function:
Example:
(open “mydata.dat” data “r”)
mydata.dat – is the name of the file (path can also be provided)
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50. I/O from/to Files
data – is the logical name that Jess associates with the file
“r” – represents the mode – how the file will be used – here read access
The open function acts as a predicate function
Returns true if the file was successfully opened
Returns false otherwise
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51. Table 8.2 File Access Modes
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52. Close Function
Once access to the file is no longer needed, it should be closed.
Failure to close a file may result in loss of information.
General format of the close function:
(close [<file-ID>])
(close data)  example
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53. Reading / Writing to a File
Which logical name used, depends on where information will be written – logical name t refers to the terminal (standard output device).
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54. Predicate Functions
A predicate function is defined to be any function that returns:
TRUE
FALSE
Any value other than FALSE is considered TRUE.
We say the predicate function returns a Boolean value.
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55. The Test Conditional Element
Processing of information often requires a loop.
Sometimes a loop needs to terminate automatically as the result of an arbitrary expression.
The test condition provides a powerful way to evaluate expressions on the LHS of a rule.
Rather than pattern matching against a fact in a fact list, the test CE evaluates an expression – outermost function must be a predicate function.
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56. Test Condition
In JESS you can drop “test” term from your test conditions.
A rule will be triggered only if all its test CEs are satisfied along with other patterns.
(<predicate-function>)
Example:
(> ?value 1)
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57. The OR Conditional Element
Consider the two rules:
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58. OR Conditional Element
These two rules can be combined into one rule – or CE requires only one CE be satisfied:
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59. The And Conditional Element
The and CE is opposite in concept to the or CE – requiring all the CEs be satisfied:
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60. Not Conditional Element
When it is advantageous to activate a rule based on the absence of a particular fact in the list, Jess allows the specification of the absence of the fact in the LHS of a rule using the not conditional element:
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61. Not Conditional We can implement this as follows:
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62. Deftemplate Attributes
Jess provides slot attributes which can be specified when deftemplate slots are defined.
Slot attributes provide strong typing and constraint checking.
One can define the allowed types that can be stored in a slot, range of numeric values.
Multislots can specify min / max numbers of fields they can contain.
Default attributes can be provided for slots not specified in an assert command.
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63. Type Attribute Defines the data types can be placed in a slot Example:
(deftemplate person
(multislot name (type SYMBOL))
(SLOT AGE (TYPE integer)))
Once defined, Jess will enforce these restrictions on the slot attributes
name – must store symbols
age – must store integers
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64. Allowed Value Attributes
Jess allows one to specify a list of allowed values for a specific type – 8 are provided:
Symbols
Strings
Lexemes
Integers
Floats
Numbers
Instance-names
Values
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65. Range Attributes
This attribute allows the specification of minimum and maximum numeric values.
Example:
(deftemplate person
(multislot name (type SYMBOL))
(slot age (type INTEGER)
(range 0 ?VARIABLE)))
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66. Salience
Jess provides two explicit techniques for controlling the execution of rules:
Salience
Modules
Salience allows the priority of rules to be explicitly specified.
The agenda acts like a stack (LIFO) – most recent activation placed on the agenda being first to fire.
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67. Salience
Salience allows more important rules to stay at the top of the agenda, regardless of when they were added.
Lower salience rules are pushed lower on the agenda; higher salience rules are higher.
Salience is set using numeric values in the range -10,000  +10,000 – zero is intermediate priority.
Salience can be used to force rules to fire in a sequential fashion.
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68. Salience
Rules of equal salience, activated by different patterns are prioritized based on the stack order of facts.
If 2+ rules with same salience are activated by the same fact, no guarantee about the order in which they will be place on the agenda.
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69. Phases and Control Facts
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70. Figure 9.2 Assignment of Salience for Different Phases
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71. Implementation of System
Approaches:
Embed the control knowledge directly into the rules.
Example:
Detection rules would include rules indicating when the isolation phase should be entered. Each group of rules would be given a pattern indicating in which phase it would be applicable.
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72. Implementation Use salience to organize the rules.
Separate the control knowledge from the domain knowledge. Each rule is given a control pattern that indicates its applicable phase. Control rules are then written to transfer control between the different phases.
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73. Salience Hierarchy
Salience hierarchy is a description of the salience values used by an expert system.
Each level corresponds to a specific set of rules whose members are all given the same salience.
When rules for detection / isolation / recovery are zero, salience hierarchy is:
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74. Misuse of Salience
Because salience is such a powerful tool, allowing explicit control over execution, there is potential for misuse.
Well-designed rule-based programs should allow inference engine to control firings in an optimal manner.
Salience should be used to determine the order when rules fire, not for selecting a single rule from a group of rules when patterns can control criteria for selection.
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75. Rule of Thumb
No more than seven salience values should ever be required for coding an expert system – bested limited to 3 – 4.
For large expert systems, programmers should use modules to control the flow of execution – limited to 2 – 3 salience values.

76. Modules
For information on modules, please refer to the “Jess Tutorial” handout.
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