Articles tagged with "autonomous-agents"
Sven Koenig wins the 2026 ACM/SIGAI Autonomous Agents Research Award - Robohub
Sven Koenig has been awarded the 2026 ACM/SIGAI Autonomous Agents Research Award, a prestigious recognition for excellence in autonomous agents research. The award highlights his influential work in AI planning and search, which has significantly shaped how intelligent agents reason and act within complex, dynamic environments. Koenig’s contributions effectively bridge theoretical foundations and practical applications, impacting not only AI and multi-agent systems but also advancing robotics through algorithms that enable robust and scalable autonomy in real-world robotic platforms. Professor Koenig holds the position of Chancellor’s Professor and Bren Chair at the Computer Science Department of UC Irvine. He is a Fellow of AAAI, AAAS, and ACM, and has earned multiple best paper awards from leading conferences such as AAAI, ICALP, and SoCS. Beyond his research, Koenig has actively contributed to the AI community through various service roles, including serving as the conference chair for AAAI 2026. The announcement was shared by AIhub, a non-profit organization dedicated to
robotautonomous-agentsAI-planningroboticsintelligent-agentsmulti-agent-systemsautonomyGenerations in Dialogue: Embodied AI, robotics, perception, and action with Professor Roberto Martín-Martín - Robohub
The article discusses the third episode of the AAAI podcast series "Generations in Dialogue: Bridging Perspectives in AI," which features a conversation between host Ella Lan and Professor Roberto Martín-Martín. The series aims to explore how generational experiences influence perspectives on AI, addressing challenges, opportunities, and ethical considerations in the field. In this episode, Martín-Martín shares insights from his childhood curiosity about technology to his current research focus on embodied AI, robotics, perception, and action. He emphasizes the importance of making robots accessible to everyone and discusses how machines can augment human capabilities, drawing inspiration from human cognition and interdisciplinary fields like psychology and cognitive science. Professor Roberto Martín-Martín is an Assistant Professor of Computer Science at the University of Texas at Austin, specializing in integrating robotics, computer vision, and machine learning to develop autonomous agents capable of real-world perception and action. His research covers a range of tasks from basic manipulation and navigation to complex activities such as cooking and mobile manipulation. With a background that includes positions
roboticsembodied-AIautonomous-agentsmachine-learningcomputer-visionhuman-robot-interactionmobile-manipulationACM SIGAI Autonomous Agents Award 2026 open for nominations - Robohub
The ACM SIGAI Autonomous Agents Research Award for 2026 is now open for nominations, with a deadline of 15 December 2025. This award recognizes excellence in research on autonomous agents, specifically honoring researchers whose current work significantly influences the field. Funded by an endowment from ACM SIGAI, the award includes a monetary prize, a certificate, and an invitation to present a plenary talk at the AAMAS 2026 conference in Paphos, Cyprus. Nominations can be submitted by anyone via a designated Google form and must include a brief statement (under one page) highlighting the nominee’s key research contributions and the impact of their ongoing work. Only explicitly nominated candidates are eligible for consideration, emphasizing the importance of proactive nominations. The winner will be announced on 1 February 2026. For further information or questions, contacts are provided through the award chair or SIGAI vice chair.
robotautonomous-agentsartificial-intelligenceACM-SIGAIrobotics-researchautonomous-systemsAI-awards#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to learn normative behaviour - Robohub
The article discusses advancements presented at IJCAI 2025 concerning the integration of Multi-Objective Reinforcement Learning (MORL) with restraining bolts to enable AI agents to learn normative behavior. Autonomous agents, powered by reinforcement learning (RL), are increasingly deployed in real-world applications such as self-driving cars and smart urban planning. While RL agents excel at optimizing behavior to maximize rewards, unconstrained optimization can lead to actions that, although efficient, may be unsafe or socially inappropriate. To address safety, formal methods like linear temporal logic (LTL) have been used to impose constraints ensuring agents act within defined safety parameters. However, safety constraints alone are insufficient when AI systems interact closely with humans, as normative behavior involves compliance with social, legal, and ethical norms that go beyond mere safety. Norms are expressed through deontic concepts—obligations, permissions, and prohibitions—that describe ideal or acceptable behavior rather than factual truths. This introduces complexity in reasoning, especially with contrary-to-duty
robotartificial-intelligencereinforcement-learningautonomous-agentssafe-AImachine-learningnormative-behaviorCongratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners - Robohub
The 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), held from May 19-23 in Detroit, recognized outstanding contributions in the field with awards for best paper, best demo, and distinguished dissertation. The Best Paper Award went to the team behind "Soft Condorcet Optimization for Ranking of General Agents," led by Marc Lanctot and colleagues. Several other papers were finalists, covering topics such as commitments in BDI agents, curiosity-driven partner selection, reinforcement learning for vehicle-to-building charging, and drone delivery systems. The Best Student Paper Award was given to works on decentralized planning using probabilistic hyperproperties and large language models for virtual human gesture selection. In addition, the Blue Sky Ideas Track honored François Olivier and Zied Bouraoui for their neurosymbolic approach to embodied cognition, while the Best Demo Award recognized a project on serious games for ethical preference elicitation by Jayati Deshmukh and team. The Victor Lesser Distinguished Dissertation Award, which highlights originality, impact, and quality in autonomous agents research, was awarded to Jannik Peters for his thesis on proportionality in selecting committees, budgets, and clusters. Lily Xu was the runner-up for her dissertation on AI decision-making for planetary health under conditions of low-quality data. These awards underscore the innovative research advancing autonomous agents and multiagent systems.
robotautonomous-agentsmultiagent-systemsdronesreinforcement-learningenergy-storageAIShlomo Zilberstein wins the 2025 ACM/SIGAI Autonomous Agents Research Award
robotautonomous-agentsmulti-agent-systemsdecision-makingreinforcement-learningresearch-awardAI