Learning Domain Knowledge to Improve Theorem Proving

摘要

We present two learning inference control heuristics for equational duduction. Based on data about facts that contributed to previous proofs, evalaution functions learn to select equations that are likely to be of use in new situations. The first evalaution function works by symbolic retrieval of generalized patterns fro ma knowledge base, the second function complies the knowledge into abstract term evalaution trees. We analyze the performance of the two heuristics on a set of examples and demonstrate their usefulness. We also show that these strategies are well suited for cooperation in the framework of the knowledge based distribution method teamwork.