Everything about Soar Cognitive Architecture totally explained
Soar (originally known as
SOAR) is a
symbolic cognitive architecture, created by
John Laird,
Allen Newell, and Paul Rosenbloom at
Carnegie Mellon University. It is both a view of what
cognition is and an implementation of that view through a
computer programming architecture for
Artificial Intelligence (AI). Since its beginnings in
1983 and its presentation on a paper in
1987 it has been widely used by AI researchers to
model different aspects of human behavior.
The main goal of the Soar project is to be able to handle the full range of capabilities of an
intelligent agent, from highly routine to extremely difficult open-ended problems. In order for that to happen, according to the view underlying Soar, it needs to be able to create
representations and use appropriate forms of knowledge (such as
procedural,
declarative,
episodic, and possibly
iconic). Soar should then address a collection of mechanisms of the
mind. Also underlying the Soar architecture is the view that a symbolic system is necessary and sufficient for general
intelligence (see brief comment on
neats versus scruffies). This is known as the
physical symbol system hypothesis. The views of cognition underlying Soar are tied to the psychological theory expressed in
Allen Newell's book,
Unified Theories of Cognition.
Although the ultimate goal for Soar is to achieve general intelligence, there's no claim that this goal has already been reached. Advocates of the system recognize that Soar is still missing some important aspects of intelligence. Currently there are projects underway to add episodic and semantic memories to Soar as well as support for emotions. Some additional examples of missing capabilities include automatically creating new representations on its own, such as through hierarchical clustering.
Soar is based on a
production system, for example it uses explicit production rules to govern its behavior (these are roughly of the form "if... then...", as also used in
expert systems). Problem solving can be roughly described as a search through a
problem space (the collection of different states which can be reached by the system at a particular time) for a
goal state (which represents the solution for the problem). This is implemented by searching for the states which bring the system gradually closer to its goal. Each move consists of a decision cycle which has an elaboration phase (in which a variety of different pieces of knowledge bearing the problem are brought to Soar's
working memory) and a decision procedure (which weighs what was found on the previous phase and assigns preferences to ultimately decide the action to be taken).
SOAR originally stood for
State,
Operator
And
Result, reflecting this representation of problem solving as the application of an operator to a state to get a result. According to the project FAQ, the Soar development community no longer regards Soar as an acronym so it's no longer spelled all in caps though it's still representative of the core of the implementation.
If the decision procedure just described isn't able to determine a unique course of action, Soar may use different strategies, known as
weak methods to solve the impasse. These methods are appropriate to situations in which knowledge isn't abundant. Some examples are
means-ends analysis (which may calculate the difference between each available option and the goal state) and a type of
hill-climbing. When a solution is found by one of these methods, Soar uses a learning technique called
chunking to transform the course of action taken into a new rule. The new rule can then be applied whenever Soar encounters the situation again (that is, there will be no longer an impasse).
ACT, for example
ACT-R is another cognitive architecture by
John R. Anderson that operates on similar principles. Other cognitive architectures are
CHREST,
CLARION,
ICARUS,
DUAL, and
Psi.
Further Information
Get more info on 'Soar Cognitive Architecture'.
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