SOAR is the
most recent cognitive architecture that was proposed by Newell
and his colleagues. The architecture is quite sophisticated and
we will gloss over much of the detail that is required to deeply
understand and evaluate this proposed architecture. The bibliography
provided at the bottom of this page provides references to the
more complete discussions of this architecture.
is a production rule system, but one that is quite differently
constructed than the one that we have already studied. The table
below taken lists six aspects of the Soar architecture that Newell
identifies as the main characteristics of SOAR. The first characteristic
is quite notable. Within a typical program there are almost always
segments of the program that are simply sequences of instructions.
In Soar there is no such code. Every task is a 'problem' which
is solved via search in the appropriate problem space. This property
is referred to as Universal Subgoaling. For Soar everything
is a problem in some problem space.
Note that the
second characteristic states that all of the systems knowledge
is stated in the form of production rules. Soar is thought of
as a kind of massive memory whose content is accessed through
the condition portion of the production rules. Consequently Soar
utilizes a variant of the Recognition Act Cycle that was
discussed earlier. However, in this case the authors of Soar
refer to am Elaboration Decision Cycle. The "working
memory" has become in Soar a much more elaborate temporary
memory structure that holds information pertaining to the state
of the current "problem solving context." The technical
details will be ignored. But think of this context as including
information relevant to the decision about what problem space
to use for the problem, what states of the space might
be under consideration, what aspects of an operator might
be under consideration, and so on. In other words the entire
process has been opened up and production rules relevant to any
of these aspects of the problem might be retrieved during the
Elaboration Phase. During the Elaboration phase all productions
that are accessed from information in this current "problem
solving context" are retrieved.
characteristic mentioned below refers to a preference language.
During the Decision Phase the rule that is to be applied must
be identified. The preference language is used to express any
preferences that apply to the set rules that have been instantiated
during the Elaboration Phase. If these is no clear preference,
then the system is at an impasse. There are several ways
in which an impasse can be encountered. Regardless, of how the
impasse is encountered, the system attempts to resolve the impasse.
Characteristic 5 below refers to this aspect of the system. Essentially
the system creates a subgoal which is to resolve the impasse.
For example, if it doesn't know which operator to apply in a
state space search, the subgoal of providing-a-basis-for-choosing-among-the-operators
would be created. Successful solution to this impasse-generated
subgoal would lead to a form of learning that the authors refer
to as chunking. Note that learning is now an intrinsic
part of the architecture; not a separate module or function.
Laird, J., Rosenbloom, P., &
Newell, A. Universal Subgoaling and Chunking: The Automatic
Generation and Learning of Goal Hierarchies. Boston: Kluwer,
Rosenbloom, P.S. & Newell,
A. The Chunking of Goal Hierarchies: A Generalized Model of Practice.
In R.S. Michalski, J.G. Carbonell, & T. Mitchell (Eds.),
Machine Learning. Vol. II. Los Altos, CA: Kaufmann, 1986.
Newell, A. Unified Theories
of Cognition. Cambridge, MA: Harvard University Press, 1990.