Symbolic Concept Acquisition Version 2

Picture of SCA2 interface
SCA is a Soar program for inductive learning that Craig Miller developed a year or two ago. I've used his ideas in my research and ended up extending SCA so now I've decided to release my changes as a new version, which I'm calling SCA2.

What is SCA?

SCA learns to assoiciate a class label with a set of features.
For example, that round, heavy objects are balls; while round, light objects are balloons.

It does this by having rules which predict certain classes :
	If the object has
		    ^color  red
		    ^size   small
		    ^weight low
	then guess
		    ^class balloon
SCA uses a search to find the rule which is most specific and returns that as its guess. The method it uses is :
  1. Check if any rules match all the object's features.
  2. If yes, then guess that value.
    If no, then remove (or abstract) one of the features and go back to step 1.

SCA learns new rules when it's given a training instance, e.g.
	^color green ^size small ^weight low => ^class balloon
It searches for the most specific rule it currently has that matches this and then learns a new rule with one more feature in it. That's the inductive step and leads it to eventually learn the class correctly. It turns out that this method models human behavior well.

What's new in SCA2?

The main changes in SCA2 are : In addition, SCA2 comes with a complete Tcl/Tk interface which allows you to

Where do I get SCA/SCA2?

Click here for a compressed tar file that contains all 3 systems, or here for a 500K uncompressed tar file or click on the individual names to get just those :

What should it look like when it's working?

These are some screen shots if you want to see what it's meant to look like.

I'm keeping a count of people who come looking for SCA2.
Counting you, the total is



Douglas Pearson [2007]