Keywords

AI; Negotiation; LLM; Prompt Engineering; Fine-Tuning; Machine Learning

Abstract

Negotiation is a complex field that can benefit from introducing artificial intelligence (AI); doing so would benefit researchers as they try to deepen their understanding of human-human and human-agent negotiation. Investigating how large language models (LLMs) can generate negotiation dialogue with emotional context would bring agents closer to acting more human. This study explores how fine-tuning and prompt engineering can achieve this goal and the possibilities for an AI that fills these criteria to be included in the Interactive Arbitration Guide Online platform (IAGO). Doing so will make the negotiation interactions in IAGO feel more complex and natural, allowing researchers to further their understanding of negotiation conversations and concepts.

Thesis Completion Year

2025

Thesis Completion Semester

Spring

Thesis Chair

Mell, Johnathan

College

College of Engineering and Computer Science

Department

Computer Science

Thesis Discipline

Computer Science

Language

English

Access Status

Open Access

Length of Campus Access

None

Campus Location

Orlando (Main) Campus

Subjects

Negotiation--Computer programs; Natural language generation (Computer science); Negotiation--Psychological aspects; Negotiation--Simulation methods; Negotiation--Study and teaching

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Rights Statement

In Copyright