Bibliographic Details
| Title: |
Machine Learning Guidance for Connection Tableaux. |
| Authors: |
Färber, Michael1, Kaliszyk, Cezary1 cezary.kaliszyk@uibk.ac.at, Urban, Josef2 |
| Source: |
Journal of Automated Reasoning. Feb2021, Vol. 65 Issue 2, p287-320. 34p. |
| Subjects: |
Monte Carlo method, Machine learning, Logic programming, Artificial intelligence, Matrices (Mathematics) |
| Abstract: |
Connection calculi allow for very compact implementations of goal-directed proof search. We give an overview of our work related to connection tableaux calculi: first, we show optimised functional implementations of connection tableaux proof search, including a consistent Skolemisation procedure for machine learning. Then, we show two guidance methods based on machine learning, namely reordering of proof steps with Naive Bayesian probabilities, and expansion of a proof search tree with Monte Carlo Tree Search. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
