[comp.ai.digest] Seminar - Acquiring Knowledge from the Outside

KALANTARI@RED.RUTGERS.EDU (06/18/87)

                     R U T G E R S     U N I V E R S I T Y

                        Department of Computer Science

                              C O L L O Q U I U M



SPEAKER:         Paul Rosenbloom
                 Stanford University

TITLE:           ACQUIRING KNOWLEDGE FROM THE OUTSIDE
                 SOME RECENT PROGRESS ON LEARNING IN SOAR

DATE:           Monday, June 29, 1987
TIME:           10:00 a.m.
PLACE:          Hill Center, Room 705

In  previous  work  on  learning in Soar we have focused on how the chunking of
internal problem solving can acquire the varieties of knowledge required  by  a
general  problem solver; for example, productions can be acquired which perform
operator retrieval, instantiation, selection, and implementation.    One  major
form  of  learning  not  covered  by  this  previous work is the acquisition of
knowledge from external sources.  In this talk  I  will  describe  two  current
projects  which  are examining how the techniques utilized in the previous work
can be employed to learn from external knowledge sources.  The first project is
working  on  the  acquisition of general search control knowledge from external
advice.    This  work  touches  on  issues  of   operationalization,   learning
apprentices, analogy, and generalization.  The second project is working on the
acquisition of declarative knowledge.  This work  demonstrates  for  the  first
time  in  Soar  what Dietterich termed "knowledge level learning"; that is, the
acquisition of knowledge not already in the system's deductive  closure.    One
implication of this demonstration is that explanation-based learning mechanisms
are not inherently limited to symbol level learning.  Issues that  have  arisen
during  this  work include: how to decouple new facts from the context in which
they were learned, how to be able to distinguish what  has  been  learned  from
what   hasn't,  and  how  to  index  declarative  information  for  appropriate
retrieval.
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