Improvements of automated deduction systems can act upon various levels of these systems. By far the biggest efficiency gains can be achieved by improving the components that guide the search. As in human problem solving, learning plays a key role in our quest for better search-guiding heuristics. In this thesis we describe and experimentally evaluate several methods for obtaining improved, or in other words learned search-guiding heuristics by exploiting proof experiences acquired in the past. A. A second method attempts to flexibly re-enact a known proof in order to lay the foundations for a proof of a so far unsolved problem.
Flexibility is achieved by realizing plain re-enactment through a focused search.
A third method is based on the concept of features, i.e., on representing mostly syntactic properties of facts with numbers, the so-called feature values. These feature values are employed to modify a given search-guiding heuristic in the light of past experience. Upon request I will be glad to send you copies of the certificates.