Eliminating combinatorics from the match in production systems (or rule-based systems) is important for expert systems, real-time performance, machine learning (particularly with respect to the utility issue), parallel implementations and cognitive modeling. In , the unique-attribute representation was introduced to eliminate combinatorics from the match. However, in so doing, unique-attributes engender a sufficiently negative set of trade-offs, so that investigating whether there are alternative representations that yield better trade-offs becomes of critical importance.
This article identifies two promising spaces of such alternatives, and explores a number of the alternatives within these spaces. The first space is generated from local syntactic restrictions on working memory. Within this space, unique-attributes is shown to be the best alternative possible. The second space comes from restrictions on the search performed during the match of individual productions (match-search). In particular, this space is derived from the combination of a new, more relaxed, match formulation (instantiationless match) and a set of restrictions derived from the constraint-satisfaction literature. Within this space, new alternatives are found that outperform unique-attributes in some, but not yet all, domains.