The Problem Space Computational Model

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The Problem Space Computational Model
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The preceding description of goals, problem spaces, states, and operators is
consistent with much of the current work in AI and cognitive psychology,
although the use of multiple problem spaces and impasse-driven goal
generation is unusual.  The commitment in Soar to using problem spaces as the
model for all symbolic goal-oriented computation is unique.  The Problem
Space Computational Model (PSCM) is based on the primitive acts that are
performed while using problem spaces to achieve a goal.  Choosing the
appropriate set of primitive acts is essential in defining the architecture.
Below we present the complete set of functions that can be carried out within
the PSCM.  These functions are grouped by the object types (goal, problem
space, state, operator) that they affect.

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