Sven Koenig

About

Sven Koenig is a Chancellor’s Professor and Bren Chair in the Department of Computer Science at UC Irvine. His research focuses on intelligent systems that operate in large, nondeterministic, nonstationary, or only partially known domains. Most of his work centers around techniques for decision making, including planning and learning, that enable single situated agents such as robots or decision-support systems, as well as teams of agents, to act intelligently in their environments and exhibit goal-directed behavior in real time. These systems are designed to function effectively even with incomplete knowledge of their environments, imperfect manipulation abilities, limited or noisy perception, or insufficient reasoning speed. His research draws on multiple fields including artificial intelligence, decision theory, and operations research, reflecting the interdisciplinary nature of his work. Applications of his research include robotics, logistics, and video games. Sven Koenig is a fellow of several professional organizations, including the Association for the Advancement of Artificial Intelligence (AAAI), the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers (IEEE), and the American Association for the Advancement of Science (AAAS). His educational background includes a Ph.D. and M.S. in Computer Science from Carnegie Mellon University, an M.S. in Computer Science from the University of California at Berkeley, and a Diplom in…

Research topics

• Computer Science
• Computer Security
• Artificial Intelligence
• Mathematics
• Mathematical optimization
• Distributed computing
• Physics

Selected publications

• Generative Curricula for Multi-Agent Path Finding via Unsupervised and Reinforcement Learning

  Journal of Artificial Intelligence Research · 2025-04-27 · 1 citations

  articleOpen accessSenior author

  Multi-Agent Path Finding (MAPF) is the challenging problem of finding collision-free paths for multiple agents, which has a wide range of applications, such as automated warehouses, smart manufacturing, and traffic management. Recently, machine learning-based approaches have become popular in addressing MAPF problems in a decentralized and potentially generalizing way. Most learning-based MAPF approaches use reinforcement and imitation learning to train agent policies for decentralized execution…

  PublisherOA PDFDOI

• Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 1 citations

  articleOpen accessSenior author

  Anytime multi-agent path finding (MAPF) is a promising approach to scalable and collision-free path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the current state-of-the-art approach where a fast initial solution is iteratively optimized by destroying and repairing selected paths of the solution. Current MAPF-LNS variants commonly use an adaptive selection mechanism to choose among multiple destroy heuristics. However, to determine promising destroy…

  PublisherOA PDFDOI

• Dynamic Incentivized Cooperation under Changing Rewards

  arXiv (Cornell University) · 2026-01-10

  preprintOpen accessSenior author

  Peer incentivization (PI) is a popular multi-agent reinforcement learning approach where all agents can reward or penalize each other to achieve cooperation in social dilemmas. Despite their potential for scalable cooperation, current PI methods heavily depend on fixed incentive values that need to be appropriately chosen with respect to the environmental rewards and thus are highly sensitive to their changes. Therefore, they fail to maintain cooperation under changing rewards in the environment…

  PublisherDOI

• Dynamic Incentivized Cooperation under Changing Rewards

  ArXiv.org · 2026-01-10

  articleOpen accessSenior author

  Peer incentivization (PI) is a popular multi-agent reinforcement learning approach where all agents can reward or penalize each other to achieve cooperation in social dilemmas. Despite their potential for scalable cooperation, current PI methods heavily depend on fixed incentive values that need to be appropriately chosen with respect to the environmental rewards and thus are highly sensitive to their changes. Therefore, they fail to maintain cooperation under changing rewards in the environment…

  PublisherOA PDF

• Judgelight: Trajectory-Level Post-Optimization for Multi-Agent Path Finding via Closed-Subwalk Collapsing

  Open MIND · 2026-01-27

  preprint

  Multi-Agent Path Finding (MAPF) is an NP-hard problem with applications in warehouse automation and multi-robot coordination. Learning-based MAPF solvers offer fast and scalable planning but often produce feasible trajectories that contain unnecessary or oscillatory movements. We propose Judgelight, a post-optimization layer that improves trajectory quality after a MAPF solver generates a feasible schedule. Judgelight collapses closed subwalks in agents' trajectories to remove redundant movement…

  DOI

Recent grants

• CPS: Small: Novel Algorithmic Techniques for Drone Flight Planning on a Large Scale

  NSF · $500k · 2018–2024

• NSF-BSF:RI:Small:Collaborative Research:Next-Generation Multi-Agent Path Finding Algorithms

  NSF · $313k · 2018–2024

• RI: Medium: Collaborative Research: Experience-Based Planning: A Framework for Lifelong Planning

  NSF · $348k · 2014–2021

Frequent coauthors

• T. K. Satish Kumar

  252 shared

• Jiaoyang Li

  215 shared

• Hang Ma

  Simon Fraser University

  159 shared

• Liron Cohen

  Ben-Gurion University of the Negev

  104 shared

• Tansel Uras

  University of Southern California

  102 shared

• Daniel Harabor

  Australian Regenerative Medicine Institute

  89 shared

• Ariel Felner

  Ben-Gurion University of the Negev

  83 shared

• Peter J. Stuckey

  78 shared

Awards & honors

• Fellow of the Association for the Advancement of Artificial…
• Fellow of the Association for Computing Machinery (ACM)
• Fellow of the Institute of Electrical and Electronics Engine…
• Fellow of the American Association for the Advancement of Sc…

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