209,205 research outputs found
The first automated negotiating agents competition (ANAC 2010)
Motivated by the challenges of bilateral negotiations between people and automated agents we organized the first automated negotiating agents competition (ANAC 2010). The purpose of the competition is to facilitate the research in the area bilateral multi-issue closed negotiation. The competition was based on the Genius environment, which is a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. The first competition was held in conjunction with the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10) and was comprised of seven teams. This paper presents an overview of the competition, as well as general and contrasting approaches towards negotiation strategies that were adopted by the participants of the competition. Based on analysis in post--tournament experiments, the paper also attempts to provide some insights with regard to effective approaches towards the design of negotiation strategies
Agents and Robots for Reliable Engineered Autonomy
This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems
Game-theoretic Objective Space Planning
Autonomous Racing awards agents that react to opponents' behaviors with agile
maneuvers towards progressing along the track while penalizing both
over-aggressive and over-conservative agents. Understanding the intent of other
agents is crucial to deploying autonomous systems in adversarial multi-agent
environments. Current approaches either oversimplify the discretization of the
action space of agents or fail to recognize the long-term effect of actions and
become myopic. Our work focuses on addressing these two challenges. First, we
propose a novel dimension reduction method that encapsulates diverse agent
behaviors while conserving the continuity of agent actions. Second, we
formulate the two-agent racing game as a regret minimization problem and
provide a solution for tractable counterfactual regret minimization with a
regret prediction model. Finally, we validate our findings experimentally on
scaled autonomous vehicles. We demonstrate that using the proposed
game-theoretic planner using agent characterization with the objective space
significantly improves the win rate against different opponents, and the
improvement is transferable to unseen opponents in an unseen environment.Comment: Submitted to 2023 IEEE International Conference on Robotics and
Automation (ICRA 2023
Multiagent Learning in Large Anonymous Games
In large systems, it is important for agents to learn to act effectively, but
sophisticated multi-agent learning algorithms generally do not scale. An
alternative approach is to find restricted classes of games where simple,
efficient algorithms converge. It is shown that stage learning efficiently
converges to Nash equilibria in large anonymous games if best-reply dynamics
converge. Two features are identified that improve convergence. First, rather
than making learning more difficult, more agents are actually beneficial in
many settings. Second, providing agents with statistical information about the
behavior of others can significantly reduce the number of observations needed.Comment: 8 pages, 2 figures. To Appear in Proceedings of the Eighth
International Conference on Autonomous Agents and Multiagent System
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