---
title: "Merging Beliefs and Counterfactuals"
authors:
  - "Emiliano Lorini"
  - "Dmitry Rozplokhas"
doi: "10.24963/ijcai.2026/440"
url: "https://ijcai.org/proceedings/2026/440"
pdf: "https://ijcai.org/proceedings/2026/0440.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3952-3960"
track: "Main Track"
keywords:
  - "Knowledge Representation and Reasoning: Belief change"
  - "Knowledge Representation and Reasoning: Knowledge representation languages"
  - "Knowledge Representation and Reasoning: Reasoning about knowledge and belief"
---

# Merging Beliefs and Counterfactuals

Emiliano Lorini, Dmitry Rozplokhas

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3952-3960. DOI: [10.24963/ijcai.2026/440](https://doi.org/10.24963/ijcai.2026/440)

## Abstract

We present a unified framework for modeling
agents’ epistemic states and counterfactual conditionals. 
The novelty of our approach lies at the
semantic level. We introduce a computationally
grounded semantics in which an agent’s doxastic
accessibility relation, needed to model the standard 
notion of deductively closed belief, is derived from the agent’s belief base, 
and in which the notion of comparative similarity between states,
needed to interpret counterfactual conditionals, is
computed from both the atomic formulas describing 
the agents’ belief bases and those describing
the environment. We use this semantics to interpret a language 
of beliefs and counterfactuals. We
demonstrate the expressiveness of our language and
the flexibility of the semantics by formalizing a
wide range of notions: counterfactual dependence,
counterfactual notions of reason and knowledge,
conditional belief. Furthermore, we show that our
semantics illuminates the subtle interplay between
belief change and counterfactuals. On the computational side, 
we provide a succinct formulation of
model checking for our language and establish a
PSPACE complexity result.
