Strategic Tool Enhanced AI Agent for Multi-Issue Negotiation (Student Abstract)

Authors

  • Daiki Kitashima Tokyo University of Agriculture and Technology National Institute of Advanced Industrial Science and Technology
  • Ryota Higa NEC Corporation National Institute of Advanced Industrial Science and Technology
  • Katsuhide Fujita Tokyo University of Agriculture and Technology National Institute of Advanced Industrial Science and Technology

DOI:

https://doi.org/10.1609/aaai.v40i48.42230

Abstract

Automated negotiation, a form of interaction among autonomous agents, plays a central role in multi-agent systems, yet the application of large language model (LLM) in this domain remains underexplored. An LLM can serve as a meta-strategist, adaptively selecting explicit strategies for execution by external strategic tools based on its capabilities. We propose a negotiation AI agent equipped with explicit strategic tools, including time-dependent and tit-for-tat negotiation strategies. Our results show that strategic tool enhanced negotiators achieve approximately 16% higher average utility compared with baseline, latest LLM negotiators.
AAAI-26 / IAAI-26 / EAAI-26 Proceedings Cover

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Published

2026-03-14

How to Cite

Kitashima, D., Higa, R., & Fujita, K. (2026). Strategic Tool Enhanced AI Agent for Multi-Issue Negotiation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41246–41248. https://doi.org/10.1609/aaai.v40i48.42230

Issue

Vol. 40 No. 48: EAAI-26 AI for Education, Model AI Assignments, AAAI-26 Emerging Trends, Doctoral Consortium, Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program