Despite abundant negotiation strategies in literature, the complexity of automated negotiation forbids a single strategy from being dominant against all others in different negotiation scenarios. To overcome this, one approach is to use mixture of experts, but at the same time, one problem of this method is the selection of experts, as this approach is limited by the competency of the experts selected. Another problem with most negotiation strategies is their incapability of adapting to dynamic variation of the opponent's behaviour within a single negotiation session resulting in poor performance. This work focuses on both, solving the problem of expert selection and adapting to the opponent's behaviour with our Autonomous Negotiating Agent Framework. This framework allows real-time classification of opponent's behaviour and provides a mechanism to select, switch or combine strategies within a single negotiation session. Additionally, 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. We demonstrate an instance of our framework by implementing maximum entropy reinforcement learning based strategies with a deep learning based opponent classifier. Finally, we evaluate the performance of our agent against state-of-the-art negotiators under varied negotiation scenarios.
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework
Australian Artificial Intelligence Institute (AAII), University of Technology Sydney（澳大利亚人工智能研究所（AAII），悉尼科技大学）；Visualisation Institute, University of Technology Sydney（可视化研究所，悉尼科技大学）
Autonomous Self-Trained Channel State Prediction Method for mmWave Vehicular Communications
Faculty of Technical Sciences, University of Novi Sad（诺维萨德大学技术科学学院）；The Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与发展研究所）；African Center of Excellence in IoT, University of Rwanda（卢旺达大学非洲物联网卓越中心）
RobEthiChor: Automated Context-aware Ethics-based Negotiation for Autonomous Robots
Department of Information Engineering and Information Sciences and Mathematics, University of L’Aquila（信息工程与信息科学及数学系，拉奎拉大学）；Department of Computer Science, Gran Sasso Science Institute（计算机科学系，格兰萨索科学研究所）
SCARCE: Emergent Institutions in Long-Horizon Multi-Agent Coding Systems
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；ELLIS Institute Tübingen（蒂宾根 ELLS 研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）
Copiloting Autonomous Multi-Robot Missions: A Game-inspired Supervisory Control Interface
Polytechnique Montreal（蒙特利尔理工学院）；NASA Jet Propulsion Laboratory, California Institute of Technology（美国国家航空航天局喷气推进实验室，加州理工学院）；University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）
A Lightweight Modular Framework for Constructing Autonomous Agents Driven by Large Language Models: Design, Implementation, and Applications in AgentForge
Andy: A Mathematical Agent for Rigorous Proof and Autonomous Research
School of Mathematical Sciences, Tongji University（同济大学数学科学学院）；Key Laboratory of Intelligent Computing and Applications (Tongji University)（同济大学智能计算与应用重点实验室）
