A Fine-Tuning Aggregation Convolutional Neural Network Surrogate Model of Strategy Selecting Mechanism for Repeated-Encounter Bilateral Automated Negotiation

Shengbo Chang, Katsuhide Fujita

2023

Abstract

Negotiation with the same opponent for multiple times for each in a different domain commonly occurs in real life. We consider this automated negotiation problem as repeated-encounter bilateral automated negotiation (RBAN), in which it is essential to learn experiences from the history of coping with the opponent. This study presents a surrogate-model-based strategy selecting mechanism that learns experiences in RBAN by fine-tuning the proposed aggregation convolutional neural network (CNN) surrogate model (ACSM). ACSM is promised to assess strategies more precisely by applying CNN to extract features from a matrix showing the outcomes’ utility distribution. It ensures the abundance of extracted features by aggregating multiple CNNs trained with diverse opponents. The fine-tuning approach adapts ACSM to the opponent in RBAN by feeding the present negotiation results to ACSM. We evaluate ACSM and the fine-tuning approach experimentally by selecting a strategy for a time-dependent agent. The experiments of negotiating with four Automated Negotiating Agents Competition (ANAC) champions and six basic agents are performed. ACSM is tested on 600 negotiation scenarios originating from ANAC domains. The fine-tuning approach is tested on 60 RBNA sessions. The experimental results indicate that ACSM outperforms an existing feature-based surrogate model, and the fine-tuning approach is able to adapt ACSM to the opponent in RBAN.

Paper Citation

in Harvard Style

Chang S. and Fujita K. (2023). A Fine-Tuning Aggregation Convolutional Neural Network Surrogate Model of Strategy Selecting Mechanism for Repeated-Encounter Bilateral Automated Negotiation. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 277-288. DOI: 10.5220/0011701300003393

in Bibtex Style

@conference{icaart23,
author={Shengbo Chang and Katsuhide Fujita},
title={A Fine-Tuning Aggregation Convolutional Neural Network Surrogate Model of Strategy Selecting Mechanism for Repeated-Encounter Bilateral Automated Negotiation},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={277-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011701300003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Fine-Tuning Aggregation Convolutional Neural Network Surrogate Model of Strategy Selecting Mechanism for Repeated-Encounter Bilateral Automated Negotiation
SN - 978-989-758-623-1
AU - Chang S.
AU - Fujita K.
PY - 2023
SP - 277
EP - 288
DO - 10.5220/0011701300003393