On Action Theories with Iterable First-Order Progression

Abstract

We study the first-order definability of progression for situation calculus action theories with a focus on the iterability of progression. Progression, the task of updating a knowledge base according to actions' effects so that proper information is retained, is notoriously challenging as it in general requires second-order logic. Exceptions where progression is first-order like local-effect actions and normal actions impose certain syntax constraints on action theories to eliminate second-order quantifiers in the progressed knowledge base. Unfortunately, the progressed result might not satisfy the constraints again, making it impossible to apply first-order progression iteratively. In this paper, we first lift the existing result on first-order progression for normal actions by allowing disjunctions in the knowledge base. As a result, we obtain an action theory whose type is called disjunctive normal, which is iteratively first-order progressable. Second, we propose a new class of action theories, called PANACK, that strictly subsumes the disjunctive normal ones, and we show that it remains iteratively first-order progressable as well.

Cite

Text

Liu and Claßen. "On Action Theories with Iterable First-Order Progression." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I14.33649

Markdown

[Liu and Claßen. "On Action Theories with Iterable First-Order Progression." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/liu2025aaai-action/) doi:10.1609/AAAI.V39I14.33649

BibTeX

@inproceedings{liu2025aaai-action,
  title     = {{On Action Theories with Iterable First-Order Progression}},
  author    = {Liu, Daxin and Claßen, Jens},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {15041-15048},
  doi       = {10.1609/AAAI.V39I14.33649},
  url       = {https://mlanthology.org/aaai/2025/liu2025aaai-action/}
}