Experiments with discrimination-tree indexing and path indexing for term retrieval
Journal of Automated Reasoning
Journal of Automated Reasoning
WALDMEISTER - High-Performance Equational Deduction
Journal of Automated Reasoning
JELIA '00 Proceedings of the European Workshop on Logics in Artificial Intelligence
RTA '95 Proceedings of the 6th International Conference on Rewriting Techniques and Applications
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Dedan: A Kernel of Data Structures and Algorithms for Automated Deduction with Equality Clauses
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Currying Second-Order Unification Problems
RTA '02 Proceedings of the 13th International Conference on Rewriting Techniques and Applications
Algorithms, Datastructures, and other Issues in Efficient Automated Deduction
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
On the Evaluation of Indexing Techniques for Theorem Proving
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
Handbook of automated reasoning
Fast Term Indexing with Coded Context Trees
Journal of Automated Reasoning
Efficient instance retrieval with standard and relational path indexing
Information and Computation - Special issue: 19th international conference on automated deduction (CADE-19)
Efficient E-Matching for SMT Solvers
CADE-21 Proceedings of the 21st international conference on Automated Deduction: Automated Deduction
Efficient instance retrieval with standard and relational path indexing
Information and Computation - Special issue: 19th international conference on automated deduction (CADE-19)
RTA'03 Proceedings of the 14th international conference on Rewriting techniques and applications
IJCAR'10 Proceedings of the 5th international conference on Automated Reasoning
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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 describe an implementation of context trees based on Curry terms and on an extension of substitution trees with equality constraints, where one also does not distinguish between internal and external variables. Experiments with matching benchmarks show that our preliminary implementation is already 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.