NegoLog: An Integrated Python-Based Automated Negotiation Framework with Enhanced Assessment Components
Abstract
The minimum vertex cover (MVC) problem is a classic NP-hard combinatorial optimization problem with extensive real-world applications. In this paper, we propose an efficient local search algorithm, InfVC, to solve the MVC in massive graphs, which comprises three ideas. First, we introduce an inference-driven optimization strategy that explores better feasible solutions through inference rules. Second, we develop a structural-determined perturbation strategy that is motivated by the structure features of high-quality solutions, prioritizing high-degree vertices into the candidate solution to guide the search process to some potential high-quality search area. Third, we design a self-adaptive local search framework that dynamically balances exploration and exploitation through a perturbation management mechanism. Extensive experiments demonstrate that InfVC outperforms all the state-of-the-art algorithms on almost massive instances.
Cite
Text
Dogru et al. "NegoLog: An Integrated Python-Based Automated Negotiation Framework with Enhanced Assessment Components." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/998Markdown
[Dogru et al. "NegoLog: An Integrated Python-Based Automated Negotiation Framework with Enhanced Assessment Components." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/dogru2024ijcai-negolog/) doi:10.24963/ijcai.2024/998BibTeX
@inproceedings{dogru2024ijcai-negolog,
title = {{NegoLog: An Integrated Python-Based Automated Negotiation Framework with Enhanced Assessment Components}},
author = {Dogru, Anil and Keskin, Mehmet Onur and Jonker, Catholijn M. and Baarslag, Tim and Aydogan, Reyhan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {8640-8643},
doi = {10.24963/ijcai.2024/998},
url = {https://mlanthology.org/ijcai/2024/dogru2024ijcai-negolog/}
}