## E 0.7

S. Schulz
Institut für Informatik, Technische Universität München, Germany
schulz@informatik.tu-muenchen.de

### Architecture

E 0.7[Sch99] is a purely equational theorem prover. The calculus used by E combines superposition (with selection of negative literals) and rewriting. For the special case that only unit clauses are present, it degenerates into unfailing completion, for the Horn case, E can simulate various positive unit strategies. No special rules for non-equational literals have been implemented, i.e., resolution is simulated via paramodulation and equality resolution.

Proof search in E is controlled by a literal selection strategy and an evaluation heuristic. A variety of selection strategies (including e.g., no selection, or select all negative literals) is available. Evaluation heuristics can be constructed on the fly by combining various parameterized primitive evaluation functions. A simple automatic mode can select strategy, term ordering, and heuristic based on simple problem characteristics.

New features since last year include a wider variety of literal selection strategies, and an improved automatic mode that now also switches between two different Knuth-Bendix orderings. A proof checker is now available as well.

### Implementation

E is implemented in ANSI C, using the GNU C compiler. The most unique feature of the implementation is the maximally shared term representation. This includes parallel rewriting for all instances of a particular subterm. A second important feature is the use of perfect discrimination trees with age and size constraints for rewriting and unit-subsumption.

The program has been successfully installed under SunOS 4.3.x, Solaris 2.x, HP-UX B 10.20, and various versions of Linux. Sources are available freely from:

        http://wwwjessen.informatik.tu-muenchen.de/~schulz/WORK/eprover.html

### Expected Competition Performance

As in the last year, we expect E to perform pretty well in all categories it participates in, with particularly strong showing for Horn problems. We have spend very little effort on the performance for unit problems, however, and it is unlikely that we can come close to Waldmeister's performance in this category.

E's auto mode is optimized for performance on the complete TPTP 2.3.0. The optimization is based on a fairly large number of test runs and consists of the selection of one of about 50 different strategies for each problem.

### References

Sch99
  Schulz S. (1999), System Abstract: E 0.3, Ganzinger H., Proceedings of the 16th International Conference on Automated Deduction (Trento, Italy), Lecture Notes in Artificial Intelligence, Springer-Verlag, pp.297--391