|
A Real-time Expert System Environment
for on-line Decision Support Applications
Technical Specifications
Backward Chaining Rules
Backward Chaining Rules define how values for DESCRIPTORs are
derived from values of other DESCRIPTORs, user inputs, simulation model
results or other information available (data bases, GIS, etc.).
Syntax:
RULE <rule#>
IF <condition>
THEN <action>
ENDRULE
<condition> := <condition <logical operator> <condition>
:= descriptor <comparative operator> <operand>
:= descriptor <singular operator>
:= TRUE
<action> := descriptor = <operand>
:= descriptor <meta operator> <constant>
<operand> := <operand> <arithmetic operator> <operand>
:= descriptor
:= <constant>
<logical operator> := AND | OR
<comparative operator> := < | > | <= | >= | == | !=
<singular operator> := EXISTS | NOT_EXISTS
<arithmetic operator> := [ | ] | * | / | + | -
<meta operator> := INCREASES_BY | DECREASES_BY
:= BECOMES
<constant> := string
:= number
Rules can result in the absolute assignment of DESCRIPTOR values,
their relative, incremental modification, or they can be used to
control the inference strategy depending on context.
Rules can also include simple formulas; more complex functions can be
used through the generic model interface.
An example for a rule used in the reservoir expert system is:
RULE 1020231
IF average_reservoir_depth == small
AND retention_time < 30
THEN reservoir_stratification = unlikely
ENDRULE
Also, the user can call up a knowledge base browser, that allows to navigate in
the tree structure of the knowledge base within the context of individual
problems. The browser can descend the inference tree, displaying sets of rules
referring to a list of DESCRIPTORs and allow to inspect individual
DESCRIPTOR definitions.
The possibility to integrate models in place of rules in an expert system
and at the same time use embedded rule-based components in models
provides a very rich repertoire of building blocks for interactive software
systems.
|