ChargingBoul: A Competitive Negotiating Agent with Novel Opponent Modeling
Joe Shymanski

TL;DR
ChargingBoul is a competitive automated negotiation agent that uses opponent classification and dynamic strategies to achieve high utility, demonstrated by its second-place finish in the 2022 ANAC competition.
Contribution
This paper introduces ChargingBoul, a novel negotiation agent that effectively balances concession and opponent modeling, advancing automated negotiation strategies.
Findings
Achieved second place in 2022 ANAC with high utility
Effectively classifies opponents based on bid patterns
Demonstrates robustness across diverse opponent strategies
Abstract
Automated negotiation has emerged as a critical area of research in multiagent systems, with applications spanning e-commerce, resource allocation, and autonomous decision-making. This paper presents ChargingBoul, a negotiating agent that competed in the 2022 Automated Negotiating Agents Competition (ANAC) and placed second in individual utility by an exceptionally narrow margin. ChargingBoul employs a lightweight yet effective strategy that balances concession and opponent modeling to achieve high negotiation outcomes. The agent classifies opponents based on bid patterns, dynamically adjusts its bidding strategy, and applies a concession policy in later negotiation stages to maximize utility while fostering agreements. We evaluate ChargingBoul's performance using competition results and subsequent studies that have utilized the agent in negotiation research. Our analysis highlights…
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Taxonomy
TopicsMulti-Agent Systems and Negotiation · Conflict Management and Negotiation · Reinforcement Learning in Robotics
