ΑΙhub.org
 

Shlomo Zilberstein wins the 2025 ACM/SIGAI Autonomous Agents Research Award


by
19 March 2025



share this:
ACM SIGAI logo

Congratulations to Shlomo Zilberstein on winning the 2025 ACM/SIGAI Autonomous Agents Research Award. This prestigious award is made for excellence in research in the area of autonomous agents. It is intended to recognize researchers in autonomous agents whose current work is an important influence on the field.

Professor Shlomo Zilberstein was recognised for his work establishing the field of decentralized Markov Decision Processes (DEC-MDPs), laying the groundwork for decision-theoretic planning in multi-agent systems and multi-agent reinforcement learning (MARL). The selection committee noted that these contributions have become a cornerstone of multi-agent decision-making, influencing researchers and practitioners alike.

Shlomo Zilberstein is Professor of Computer Science and former Associate Dean of Research at the University of Massachusetts Amherst. He is a Fellow of AAAI and the ACM, and has received numerous awards, including the UMass Chancellor’s Medal, the IFAAMAS Influential Paper Award, and the AAAI Distinguished Service Award.



tags: ,


AIhub is dedicated to free high-quality information about AI.
AIhub is dedicated to free high-quality information about AI.

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

coffee corner

AIhub coffee corner: World models

  22 May 2026
The AIhub coffee corner captures the musings of AI experts over a short conversation.

Why the world’s banks are so worried about Anthropic’s latest AI model

  21 May 2026
The finance world’s concern rests on the impressive cyber capabilities of a product called Mythos.

Embracing empiricism – from the lottery hypothesis to creating real-world impact: an interview with Jonathan Frankle

  20 May 2026
Jonathan Frankle discusses empiricism, making an impact, and the legacy of his lottery ticket hypothesis.

A faster way to estimate AI power consumption

  19 May 2026
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.

Introducing ARFBench: A time series question-answering benchmark based on real incidents

  18 May 2026
To resolve system failures, engineers must troubleshoot outages quickly.

Does ‘federated unlearning’ in AI improve data privacy, or create a new cybersecurity risk?

  15 May 2026
As the capacity of AI systems increases apace, so do concerns about the privacy of user data.

Reflections from #AIES2025

and   14 May 2026
We reflect on AIES 2025, outlining a discussion session on LLMs for clinical usage and human rights.

Deep learning-powered biochip to detect genetic markers

System can detect extremely small amounts of microRNAs, genetic markers linked to diseases such as heart disease.



AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence