Introduction to CLIPS

1
Introduction to CLIPS
2
What is CLIPS?

• CLIPS is a multiparadigm programming language
  that provides support for
• Rule-based
• Object-oriented
• Procedural programming
• Syntactically, CLIPS resembles
• Eclipse
• CLIPS/R2
• JESS

3
Other CLIPS Characteristics

• CLIPS supports only forward-chaining rules.
• The OOP capabilities of CLIPS are referred to as
  CLIPS Object-Oriented Language (COOL).
• The procedural language capabilities of CLIPS are
  similar to languages such as
• C
• Ada
• Pascal
• Lisp

4
CLIPS Characteristics

• CLIPS is an acronym for C Language Integrated
  Production System.
• CLIPS was designed using the C language at the
  NASA/Johnson Space Center.
• CLIPS is portable PC ? CRAY.

5
CLIPS Notation

• Symbols other than those delimited by lt gt, ,
  or should be typed exactly as shown.
• mean the contents are optional and lt gt mean
  that a replacement is to be made.
• following a description means that the
  description can be replaced by zero or more
  occurrences of the specified value.

6
CLIPS Notation

• Descriptions followed by mean that one or more
  values specified by description should be used in
  place of the syntax description.
• A vertical bar indicates a choice among one or
  more of the items separated by the bars.

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Fields

• To build a knowledge base, CLIPS must read input
  from keyboard / files to execute commands and
  load programs.
• During the execution process, CLIPS 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.

8
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.

9
Symbol Fields

• Symbols begin with printable ASCII characters
  followed by zero or more characters, followed by
  a delimiter.
• CLIPS is case sensitive.

10
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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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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Entering / Exiting CLIPS

• The CLIPS prompt is CLIPSgt
• This is the type-level mode where commands can be
  entered.
• To exit CLIPS, one types CLIPSgt (exit) ?
• CLIPS will accept input from the user / evaluate
  it / return an appropriate response
• CLIPSgt ( 3 4) ? ? value 7 would be returned.

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Facts and CLIPS

• To solve a problem, CLIPS 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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Example Fact in CLIPS
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Deftemplate

• Before facts can be constructed, CLIPS 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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Deftemplate General Format
17
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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Adding Facts

• CLIPS 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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Displaying Facts

• CLIPSgt (facts) ?

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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
• CLIPSgt (retract 2) ?

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Modifying Facts

• Slot values of deftemplate facts can be modified
  using the modify command

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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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Watch Command

• The watch command is useful for debugging
  purposes.
• If facts are watched, CLIPS 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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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 ltdeffacts namegt ltoptional comment
• ltfactsgt )
• The reset command is used to assert the facts in
  a deffacts statement.

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The Components of a Rule

• To accomplish work, an expert system must have
  rules as well as facts.
• Rules can be typed into CLIPS (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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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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Rule Components

• The rule would be shown as follows
• (defrule fire-emergency An example rule
• (emergency (type fire))
• gt
• (assert (response
• (action activate-sprinkler-system))))

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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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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 gt 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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The Agenda and Execution

• To run the CLIPS program, use the run command
• CLIPSgt (run ltlimitgt)?
• -- the optional argument ltlimitgt is the maximum
  number of rules to be fired if omitted, rules
  will fire until the agenda is empty.

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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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What is on the Agenda?

• To display the rules on the agenda, use the
  agenda command
• CLIPSgt (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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Command for Manipulating Constructs

• The list-defrules command is used to display the
  current list of rules maintained by CLIPS.
• The list-deftemplates displays the current list
  of deftemplates.
• The list-deffacts command displays the current
  list of deffacts.
• The ppdefrule, ppdeftemplate and ppdeffacts
  commands display the text representations of a
  defrule, deftemplate, and a deffact, respectively.

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Commands

• The undefrule, undeftemplate, and undeffacts
  commands are used to delete a defrule, a
  deftemplate, and a deffact, respectively.
• The clear command clears the CLIPS environment
  and adds the initialfact-defacts to the CLIPS
  environment.
• The printout command can also be used to print
  information.

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Other Commands

• Load allows loading of rules from an external
  file.
• Save opposite of load, allows saving of
  constructs to disk

36
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)

37
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
  lt-.
• 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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Field Constraints

• In addition to pattern matching capabilities and
  variable bindings, CLIPS 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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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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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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Combining Field Constraints

• Field constraints can be used together with
  variables and other literals to provide powerful
  pattern matching capabilities.
• Example 1 ?eyes1bluegreen
• This constraint binds the persons eye color to
  the variable, ?eyes1 if the eye color of the fact
  being matched is either blue or green.
• Example 2 ?hair1black
• This constraint binds the variable ?hair1 if the
  hair color of the fact being matched is not
  black.

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Functions and Expressions

• CLIPS has the capability to perform calculations.
• The math functions in CLIPS are primarily used
  for modifying numbers that are used to make
  inferences by the application program.

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Numeric Expressions in CLIPS

• Numeric expressions are written in CLIPS in
  LISP-style using prefix form the operator
  symbol goes before the operands to which it
  pertains.
• Example 1
• 5 8 (infix form) ? 5 8 (prefix form)
• Example 2
• (infix) (y2 y1) / (x2 x1) gt 0
• (prefix) (gt ( / ( - y2 y1 ) (- x2 x1 ) )
  0)

44
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.

45
Variable Numbers of Arguments

• Many CLIPS functions accept variable numbers of
  arguments.
• Example
• CLIPSgt (- 3 5 7) ? returns 3 - 5 -2 - 7 -9
• There is no built-in arithmetic precedence in
  CLIPS everything is evaluated from left to
  right.
• To compensate for this, precedence must be
  explicitly written.

46
Embedding Expressions

• Expressions may be freely embedded within other
  expressions

47
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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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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Summing Values
50
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 ltvariablegt ltvaluegt)

51
I/O Functions

• When a CLIPS program requires input from the user
  of a program, a read function can be used to
  provide input from the keyboard

52
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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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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I/O from/to Files

• data is the logical name that CLIPS 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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Table 8.2 File Access Modes
56
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 ltfile-IDgt)
• (close data) ? example

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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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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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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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Test Condition

• A rule will be triggered only if all its test CEs
  are satisfied along with other patterns.
• (test ltpredicate-functiongt)
• Example
• (test (gt ?value 1))

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Predicate Field Constraint

• The predicate field constraint allows for
  performing predicate tests directly within
  patterns.
• The predicate field constraint is more efficient
  than using the test CE.
• It can be used just like a literal field
  constraint by itself or part of a complex
  field.
• The predicate field constraint is always followed
  by a function for evaluation (predicate function).

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Return Value Constraint

• The return value constraint allows the return
  value of a function to be used for comparison
  inside a pattern.
• The return value constraint must be followed by a
  function (not necessarily a predicate function).
• The function must have a single-field return
  value.

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The OR Conditional Element

• Consider the two rules

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OR Conditional Element

• These two rules can be combined into one rule
  or CE requires only one CE be satisfied

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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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Not Conditional Element

• When it is advantageous to activate a rule based
  on the absence of a particular fact in the list,
  CLIPS allows the specification of the absence of
  the fact in the LHS of a rule using the not
  conditional element

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Not Conditional

• We can implement this as follows

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Deftemplate Attributes

• CLIPS 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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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, CLIPS will enforce these
  restrictions on the slot attributes
• name must store symbols
• age must store integers

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Allowed Value Attributes

• CLIPS 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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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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Salience

• CLIPS 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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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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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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Phases and Control Facts
76
Figure 9.2 Assignment of Salience for Different
Phases
77
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.

78
Implementation

1. Use salience to organize the rules.
2. 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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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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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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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.