HT Tutorial Exercise 8 - Create an Op-Implementation Problem Space

• Learn how to add knowledge to resolve an operator no-change impasse.

 

Watch an operator no-change impasse

 

This exercise involves adding to the rules in file ht.s8. Before you start, make a copy of the file -- for example by clicking on the filename just given, and using the Save As... facility of your browser -- and call it something like "myht2.s8". Continue working with a Soar with the rules from file "ht.s8" loaded (or start a fresh Soar and load either the "ht.s8" file or the copy of it you have just made). Now

excise   ht*apply-op*eat

Now try running the model (for several decision cycles) through a new impasse that will appear, that of an operator no-change. This impasse occurs when an operator has been selected but the operator does not lead to any change to the state. You can compare your trace with Trace 8. This impasse can be seen in very fine detail by setting the watch level to 3 or 4.

 

Creating an operator implementation problem space to solve the impasse

 

In order to fix this impasse, you will need to write several productions (all straightforward) that create a problem space to implement the operator. Write them in the file "myht2.s8" (or whatever you called it). Productions will be needed to:

 
• Create and propose an 'eating' problem space, somewhat like how default*selection*propose*space*selection proposes the selection space (but remove the :default keyword that appears in the production).

   

• Propose, in this case, an exceedingly simple operator to implement the super-state's operator (eat). If you print a state, you will see how it keeps track of its superstate. The rule will look somewhat like ht*propose-op*eat.

   

• Apply the sub-problem space eating operator to the super-state (like ht*apply*eat, except modify the super-state instead of the state).

When you've loaded and run your code, you should get a chunk that looks a lot like ht*apply-op*eat.

 

Follow up questions

 

1.  
2. Does chunking just provide speedup learning?

    

3. Does chunking just provide deductive closure?

    

4. Could chunking also provide an inductive closure?