HINTON@CMU-CS-C.ARPA (04/19/84)
From: Geoff Hinton <HINTON@CMU-CS-C.ARPA>
[Forwarded from the CMU-AI bboard by Laws@SRI-AI.]
"RECENT PROGRESS IN SOAR: CHUNKING AND R1-SOAR"
by John Laird & Paul Rosenbloom
AI Seminar, Tuesday April 24, 4.00pm, Room 5409
In this talk we present recent progress in the development of the @p[Soar]
problem-solving architecture as a general cognitive architecture. This work
consists of first steps toward: (1) an architecture that can learn about all
aspects of its own behavior (by extending chunking to be a general learning
mechanism for @p[Soar]); and (2) demonstrating that @p[Soar] is (more than)
adequate as a basis for knowledge-intensive (expert systems) programs.
Until now chunking has been a mechanism that could speed up simple
psychological tasks, providing a model of how people improve their
performance via practice. By combining chunking with @p[Soar], we show how
chunking can do the same for AI tasks such as the Eight Puzzle, Tic-Tac-Toe,
and a portion of an expert system. More importantly, we present partial
demonstrations: (1) that chunking can lead to more complex forms of
learning, such as the transfer of learned behavior (that is, the learning of
generalized information), and strategy acquisition; and (2) that it is
possible to build a general problem solver that can learn about all aspects
of its own behavior.
Knowlege-intensive programs are built in @p[Soar] by representing basic task
knowledge as problem spaces, with expertise showing up as rules that guide
complex problem-space searches and substitute for expensive problem-space
operators. Implementing a knowledge-intensive system within @p[Soar] begins
to show how: (1) a general problem-solving architecture can work at the
knowledge intensive (expert system) end of the problem solving spectrum; (2)
it can integrate basic reasoning and expertise, using both search and
knowledge when relevant; and (3) it can perform knowledge acquisition by
transforming computationally intensive problem solving into efficient
expertise-level rules (via chunking). This approach is demonstrated on a
portion of the expert system @p[R1], which configures computers.