ChargingBoul: A Competitive Negotiating Agent with Novel Opponent Modeling

• URL: http://arxiv.org/abs/2512.06595v1
• Date: Sat, 06 Dec 2025 23:32:11 GMT
• Title: ChargingBoul: A Competitive Negotiating Agent with Novel Opponent Modeling
• Authors: Joe Shymanski,
• Abstract summary: This paper presents ChargingBoul, a negotiating agent that competed in the 2022 Automated Negotiating Agents Competition (ANAC)<n> ChargingBoul employs a lightweight yet effective strategy that balances concession and opponent modeling to achieve high negotiation outcomes.<n>We evaluate ChargingBoul's performance using competition results and subsequent studies that have utilized the agent in negotiation research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ChargingBoul's effectiveness across diverse opponent strategies and its contributions to advancing automated negotiation techniques. We also discuss potential enhancements, including more sophisticated opponent modeling and adaptive bidding heuristics, to improve its performance further.

Related papers

• EQ-Negotiator: Dynamic Emotional Personas Empower Small Language Models for Edge-Deployable Credit Negotiation [66.09161596959771]
  Small language models (SLMs) offer a practical alternative, but suffer from a significant performance gap compared to large language models (LLMs)<n>This paper introduces EQ-Negotiator, a novel framework that bridges this capability gap using emotional personas.<n>We show that a 7B parameter language model with EQ-Negotiator achieves better debt recovery and negotiation efficiency than baseline LLMs more than 10 times its size.
  arXiv  Detail & Related papers  (2025-11-05T11:25:07Z)
• EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation [61.627248012799704]
  Existing Large Language Models (LLMs) agents largely overlook the functional role of emotions in such negotiations.<n>We present EvoEmo, an evolutionary reinforcement learning framework that optimize dynamic emotional expression in negotiations.
  arXiv  Detail & Related papers  (2025-09-04T15:23:58Z)
• EmoDebt: Bayesian-Optimized Emotional Intelligence for Strategic Agent-to-Agent Debt Recovery [65.30120701878582]
  Large Language Model (LLM) agents are vulnerable to exploitation in emotion-sensitive domains like debt collection.<n>EmoDebt is an emotional intelligence engine that reframes a model's ability to express emotion in negotiation as a sequential decision-making problem.<n>EmoDebt achieves significant strategic robustness, substantially outperforming non-adaptive and emotion-agnostic baselines.
  arXiv  Detail & Related papers  (2025-03-27T01:41:34Z)
• ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization [5.092928597354372]
  ASTRA is a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity.<n>ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a tool-integrated action with a linear programming (LP) solver, and (3) selecting offers based on strategy assessment and the partner's acceptance probability.
  arXiv  Detail & Related papers  (2025-03-10T09:57:50Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising [53.636153252400945]
  We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
  arXiv  Detail & Related papers  (2021-06-11T08:07:14Z)
• An Autonomous Negotiating Agent Framework with Reinforcement Learning Based Strategies and Adaptive Strategy Switching Mechanism [3.4376560669160394]
  This work focuses on solving the problem of expert selection and adapting to the opponent's behaviour with our Autonomous Negotiating Agent Framework. Our framework has a reviewer component which enables self-enhancement capability by deciding to include new strategies or replace old ones with better strategies periodically.
  arXiv  Detail & Related papers  (2021-02-06T14:38:03Z)
• Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues [6.12762193927784]
  We present a novel bilateral negotiation model that allows a self-interested agent to learn how to negotiate over multiple issues. The model relies upon interpretable strategy templates representing the tactics the agent should employ during the negotiation. It learns template parameters to maximize the average utility received over multiple negotiations, thus resulting in optimal bid acceptance and generation.
  arXiv  Detail & Related papers  (2020-09-17T13:52:18Z)
• Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents [65.2200847818153]
  In a competitive scenario, the agent does not only have a dynamic environment but also is directly affected by the opponents' actions. Observing the Q-values of the agent is usually a way of explaining its behavior, however, do not show the temporal-relation between the selected actions.
  arXiv  Detail & Related papers  (2020-07-30T11:30:42Z)
• Automated Configuration of Negotiation Strategies [0.0]
  Bidding and acceptance strategies have a substantial impact on the outcome of negotiations in scenarios with linear additive and nonlinear utility functions. We develop a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings. We show that our automatically configured agent outperforms all other agents, with a 5.1% increase in negotiation payoff compared to the next-best agent.
  arXiv  Detail & Related papers  (2020-03-31T20:31:33Z)
• A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation [6.484413431061962]
  We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed.
  arXiv  Detail & Related papers  (2020-01-31T12:05:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.