Lisbon, Portugal. July 20-23, 2026.
ISSN: 2334-1033
ISBN: 978-1-956792-18-8
Copyright © 2026 International Joint Conferences on Artificial Intelligence Organization
First-order logic is the basis for many knowledge representation formalisms and methods. Providing technological support for learning to write first-order formulas for natural language specifications requires methods to test formulas for (non-)equivalence and to provide explanations for non-equivalence. We propose such methods based on both theoretical insights and existing tools, implement them, and report on experiments testing their effectiveness on a large educational data set with > 100.000 pairs of first-order formulas.