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キーワード:
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要旨:
Indexing data structures have a crucial impact on
the performance of automated theorem provers. Examples are discrimination
trees, which are like tries where terms are seen as strings and common prefixes
are shared, and substitution trees, where terms keep their tree structure and
all common contexts can be shared. Here we describe a new indexing data
structure, called context trees, where, by means of a limited kind of context
variables, also common subterms can be shared, even if they occur below
different function symbols. Apart from introducing the concept, we also provide
evidence for its practical value. We show how context trees can be implemented
by means of abstract machine instructions. Experiments with matching benchmarks
show that our implementation is competitive with tightly coded current
state-of-the-art implementations of the other main techniques. In particular
space consumption of context trees is significantly less than for other index
structures.
