ML Anthology

Sampling-Based Belief Revision

Michael Thielscher
IJCAI 2016 pp. 1272-1278
/ijcai/2016/thielscher2016ijcai-sampling/

Abstract

Model sampling has proved to be a practically viable method for decision-making under uncertainty, for example in imperfect-information games with large state spaces. In this paper, we examine the logical foundations of sampling-based belief revision. We show that it satisfies six of the standard AGM postulates but not Vacuity nor Subexpansion. We provide a corresponding representation theorem that generalises the standard result from a single to a family of faithful assignments for a given belief set. We also provide a formal axiomatisation of sampling-based belief revision in the Situation Calculus as an alternative way of reasoning about actions, sensing, and beliefs. PDF

PDF IJCAI Semantic Scholar

Cite

Text

Thielscher. "Sampling-Based Belief Revision." International Joint Conference on Artificial Intelligence, 2016.

Markdown

[Thielscher. "Sampling-Based Belief Revision." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/thielscher2016ijcai-sampling/)

BibTeX

@inproceedings{thielscher2016ijcai-sampling,
  title     = {{Sampling-Based Belief Revision}},
  author    = {Thielscher, Michael},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {1272-1278},
  url       = {https://mlanthology.org/ijcai/2016/thielscher2016ijcai-sampling/}
}