A Real-time Expert System Environment
for on-line Decision Support Applications

Technical Specifications

DESCRIPTORs

The facts (data) of RTXPS are stored in DESCRIPTORs.

A value is assigned to a DESCRIPTOR either by direct editing or by starting the rule-based inference. The system then uses a set of alternative methods enumerated in the DESCRIPTOR definition to obtain or update the DESCRIPTOR value in the current context. The inference engine compiles all necessary information for the appropriate Backward Chaining Rules' input conditions recursively, evaluates the Backward Chaining Rules, and eventually updates the target DESCRIPTOR.

The complete syntax of a DESCRIPTOR is:

       DESCRIPTOR
       <descriptor_name>
       A  <alias_for_descriptor_name>
       T  <descriptor_type>
       U  <unit>
       V  <range> / <range> / <range> / ...
       R  <rule#> / <rule#> / ...
       TB <table#> / <table#> / ...
       F  <function>
       IF <interface function>
       G  <gis_function> <gis_overlay>
       Q  <question>
       MODEL
       <model_name>
       T <model_type>
       I <input_descriptor> / <input_descriptor> /
       O <output_descriptor> / <output_descriptor> /
       ENDMODEL
       ALTERNATIVE
       <alternative>
       <alternative defs>
       ENDALTERNATIVE
       LAYOUT
       X <window x-coordinate>
       Y <window y-coordinate>
       WIDTH <window width>
       HEIGHT <window height>
       BGCOLOR <window bgcolor>
       BORDER_WIDTH <window borderwidth>
       BORDER_COLOR <window bordercolor>
       FORMAT <value selector format_string>
       DELTA <value selector increment>
       HYPER_INFO <hyperinfo path> 
       HYPER_X <hyperinfo x-coordinate>
       HYPER_Y <hyperinfo x-coordinate>
       HYPER_WIDTH <hyperinfo width>
       HYPER_HEIGHT <hyperinfo height>
       HYPER_TWIDTH <hyperinfo backgroundwin width>
       HYPER_THEIGHT <hyperinfo backgroundwin height>
       HYPER_FGCOLOR <hyperinfo foreground color>
       HYPER_BGCOLOR <hyperinfo background color>
       HYPER_KEYCOLOR <hyperinfo keyword color>
       HYPER_HIKEYCOLOR <hyperinfo highlight color>
       HYPER_SWBORDERC <hyperinfo BORDER="1" color>
       ENDLAYOUT
       ENDDESCRIPTOR

A simple example for a DESCRIPTOR of the reservoir expert system is retention_time:

 DESCRIPTOR
 retention_time
 T S
 U days
 V very_small[   0,  360] / 
 V small     [ 360, 1080] / 
 V medium    [1080, 1800] /
 V large     [1800, 3600] / 
 V very_large[3600, 7200] /
 R 7777007 /
 Q What is the average retention time, in days, 
 Q for the reservoir ? rtention time is the theoretical
 Q period the average volume of water spends in the reservoir,
 Q estimated as the ratio of volume to throughflow.
 ENDDESCRIPTOR

A typical use of this inference process is to assist the user in specifying scenario parameters: here the system collects circumstantial evidence to derive an informed guess where no hard data are available.

Another use of the backward chaining capabilities of the expert system is to provide a synthesis of large model generated data volumes. The chain of models used to simulate an accident scenario may easily generate data volumes in the order of Gigabytes. These should, however, be summarized in a few simple variables such as the number of people exposed, the level of exposure, the area contaminated, estimated material damage and a rough classification of the accident: these classifications are needed to trigger the appropriate responses.

The flexibility to use, alternatively or conjunctively, both qualitative symbolic and quantitative numerical methods in one and the same application allows the system to be responsive to the information at hand, and the users requirements and constraints. This combination of methods of analysis, and the integration of data bases, geographical information systems, and hypertext, allows to efficiently exploit whatever information, data and expertise is available in a given problem situation.

An example for a DESCRIPTOR of the reservoir expert system with an external model (in this particular case the inflow_model) is mean_annual_inflow:

DESCRIPTOR
mean_annual_inflow
T S
U Mill.m3
V very_small[0,30] / small[30,150] / medium[150,3000] /
V large[3000,30000] / very_large[30000,300000] /
MODEL
inflow_model
T local_wait
I hemisphere / east_west / longitude / latitude /
O mean_annual_inflow /
ENDMODEL
Q what is the long term average mean annual inflow,
Q in Million meter cubed, to the reservoir
ENDDESCRIPTOR

A model of human problem solving recursively refines and redefines a problem as more information becomes available or certain alternatives are excluded. This responsiveness to the problem situation and the information available, and the ability to adjust as more information becomes available, that is in a sense, learn, is a characteristic of intelligent systems.