Stone, Peter

Multiagent systems: A survey from a machine learning perspective

Stone, P., & Veloso, M. (2000) · Autonomous Robots

Multi-Agent Systems and NegotiationReinforcement Learning in Robotics

Cited by 1,218

Autonomous agents modelling other agents: A comprehensive survey and open problems

Albrecht, S. V., & Stone, P. (2018) · Artificial Intelligence

Multi-Agent Systems and NegotiationLogic, Reasoning, and Knowledge

Cited by 417

Ad Hoc Autonomous Agent Teams: Collaboration without Pre-Coordination

Stone, P., Kaminka, G., Kraus, S., & Rosenschein, J. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence

Multi-Agent Systems and NegotiationTeam Dynamics and Performance

Cited by 323

Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles

Cui, J., Qiu, H., Chen, D., Stone, P., & Zhu, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Autonomous Vehicle Technology and SafetyVehicular Ad Hoc Networks (VANETs)

Cited by 138

Team-partitioned, opaque-transition reinforcement learning

Stone, P., & Veloso, M. (1999) · Proceedings of the third annual conference on Autonomous Agents

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 114

Principles and Guidelines for Evaluating Social Robot Navigation Algorithms

Francis, A., Pérez-D’Arpino, C., Li, C., Xia, F., Alahi, A., Alami, R., Bera, A., Biswas, A., Biswas, J., Chandra, R., et al. (2025) · ACM Transactions on Human-Robot Interaction

Social Robot Interaction and HRIRobotic Path Planning Algorithms

Cited by 107

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J., & Stone, P. (2023) · arXiv

AI-based Problem Solving and PlanningNatural Language Processing Techniques

Cited by 104

Protecting against evaluation overfitting in empirical reinforcement learning

Whiteson, S., Tanner, B., Taylor, M. E., & Stone, P. (2011) · 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 86

A collective AI via lifelong learning and sharing at the edge

Soltoggio, A., Ben-Iwhiwhu, E., Braverman, V., Eaton, E., Epstein, B., Ge, Y., Halperin, L., How, J., Itti, L., Jacobs, M. A., et al. (2024) · Nature Machine Intelligence

Domain Adaptation and Few-Shot LearningIoT and Edge/Fog Computing

Cited by 51

Bottom-Up Skill Discovery From Unsegmented Demonstrations for Long-Horizon Robot Manipulation

Zhu, Y., Stone, P., & Zhu, Y. (2022) · IEEE Robotics and Automation Letters

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 50

Models of human preference for learning reward functions

Knox, W. B., Hatgis-Kessell, S., Booth, S., Niekum, S., Stone, P., & Allievi, A. (2022) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 8

Learning Generalizable Manipulation Policies with Object-Centric 3D Representations

Zhu, Y., Jiang, Z., Stone, P., & Zhu, Y. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 2

Longhorn: State Space Models are Amortized Online Learners

Liu, B., Wang, R., Wu, L., Feng, Y., Stone, P., & Liu, Q. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0