|CSC 261||Grinnell College||Fall, 2007|
This lab explores elements of the TMYCIN expert system by Gordon Novak at the University of Texas. The goal is to become sufficiently familiar with this approach that we can use it to build an expert system to place incoming Grinnell students in statistics. (Later, we will tackle the same problem using a neural-network approach. We then will be able to make comparisons about these two general approaches.)
All programs and documentation used in this lab may be found in directory ~walker/261/tmycin/
Copy files tmycin.lsp, rocks.lsp, and rocks.tst to your account.
Run a few sample runs, as follows:
(load "tmycin.lsp") (load "rocks.lsp") (doconsult)
Add a new rule that identifies the rock "topaz" as being yellow and having a hardness of 8 (on the nonlinear Mohs Scale used in geology).
Add another new rule that identifies "quartz" as being grey and having a hardness between 6 and 8.
Review the code for the TMYCIN inference engine (in tmycin.lsp). In your review, note that the get procedure retrieves property information for a variable. For example, (get parm 'rules) retrieves the property list for rules from the variable parm.
Copy files snakes.lsp and snakes.tst to your account.
Run the snakes rule base (snakes.lsp), again using the TMYCIN inference engine (tmycin.lsp).
This expert system tries to determine the identify and Latin name of a snake and also whether or not the snake is poisonous.
Describe briefly two interactions with the expert system.
In several test runs, suppose your goal is to determine whether or not a snake is poisonous. Determine how little informtion you can supply and still determine if the snake is poisonous.
Review the properties and structure that comprise the context of each snake object. Explain briefly:
This document is available on the World Wide Web as
created 2 November 2007|
last revised 2 November 2007
|For more information, please contact Henry M. Walker at (firstname.lastname@example.org)|