From 34e6456ec145ece57844ebaf75b078a1c080787b Mon Sep 17 00:00:00 2001
From: Arseny Skryagin <37047795+askrix@users.noreply.github.com>
Date: Fri, 20 Dec 2024 11:49:07 +0100
Subject: [PATCH] Update references.bib

Added `Crossref` to `skryagin2024asn`
---
 references.bib | 1 +
 1 file changed, 1 insertion(+)

diff --git a/references.bib b/references.bib
index db02a5f..07574bd 100644
--- a/references.bib
+++ b/references.bib
@@ -4,6 +4,7 @@ @misc{skryagin2024asn
   author={Arseny Skryagin and Daniel Ochs and Phillip Deibert and Simon Kohaut and Devendra Singh Dhami and Kristian Kersting},
   Note={Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU-bound nature of state-of-the-art solvers. To this end, we propose Answer Set Networks (ASN), a NeSy solver. Based on Graph Neural Networks (GNN), ASNs are a scalable approach to ASP-based Deep Probabilistic Logic Programming (DPPL). Specifically, we show how to translate ASPs into ASNs and demonstrate how ASNs can efficiently solve the encoded problem by leveraging GPU's batching and parallelization capabilities. Our experimental evaluations demonstrate that ASNs outperform state-of-the-art CPU-bound NeSy systems on multiple tasks. Simultaneously, we make the following two contributions based on the strengths of ASNs. Namely, we are the first to show the finetuning of Large Language Models (LLM) with DPPLs, employing ASNs to guide the training with logic. Further, we show the "constitutional navigation" of drones, i.e., encoding public aviation laws in an ASN for routing Unmanned Aerial Vehicles in uncertain environments.}, 
   Keywords={Answer Set Programming, Deep Learning, Neuro-Symbolic AI, Large Language Models},
+  Crossref={https://github.com/ml-research/answersetnetworks},
   year={2024},
   eprint={2412.14814},
   Howbulished={arXiv preprint arXiv:2412.14814},