---
title: "Sampling-Based Belief Revision"
authors:
  - "Michael Thielscher"
doi: ""
url: "https://ijcai.org/proceedings/2016/184"
pdf: "https://ijcai.org/proceedings/2016/0184.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1272-1277"
track: ""
keywords:
---

# Sampling-Based Belief Revision

Michael Thielscher

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1272-1277.

## 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.