Hi there
Having , I have come to know that in order to make a decision tree , it is best to represent knowlodge base in the form of facts and not to put them in static rules (as is done in the animal.clp example in clips 6.4) . However I do not seem to understand how the file "animal.clp" actually works , as the flow of information makes it hard to watch all the activations and ...
could somebody please walk me through it ?
I would also like to thank Mr.Riley for his efforts don the project
The file is attached
The startup rule focus on the CHAIN module (which generates goals) and the ASK module (which asks questions to satisfy goals).
The propagate-goal rule determines that the superphylum value is needed to determine the type.animal value so a goal is generated.
The propagate-goal rule determines that the backbone value is needed to determine the superphylum value so a goal is generated.
The propagate-goal rule determines that the backbone value is needed to determine the superphylum value, but no goal is generated because one already exists.
The propagate-goal rule determines that the superphylum value is needed to determine the type.animal value, but no goal is generated because one already exists.
The ask-question-legalvalues rule asks a question to determine the backbone value.
The continue rules focuses on the CHAIN and ASK modules since new information is available.
The rule-satisfied-is rule removes the "backbone is yes" condition from rules requiring that condition.
The apply-rule rule applies the actions of the rule with the "backbone is yes" condition since it is now satisfied and the superphylum value is set to backbone.
The rule-satisfied-is rule removes the "superphylum is backbone" condition from rules requiring that condition.
The apply-rule rule applies the actions of the rule with the "superphylum is backbone" condition since it is now satisfied and the type.animal value is set to cow.
The goal-satisfied rule fires since the value for type.animal has been determined and that is the value specified by the answer fact. A message is printed specifying the determined value.
Hi there
Having , I have come to know that in order to make a decision tree , it is best to represent knowlodge base in the form of facts and not to put them in static rules (as is done in the animal.clp example in clips 6.4) . However I do not seem to understand how the file "animal.clp" actually works , as the flow of information makes it hard to watch all the activations and ...
could somebody please walk me through it ?
I would also like to thank Mr.Riley for his efforts don the project
The file is attached
Attached is a shortened version of the CLIPS 6.4 program that is easier to step through.
Thanks a lot for your explanation
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