ALA 2026
25 & 26 May 2026, Paphos, Cyprus
News
- 10 Dec 2025: ALA 2026 website goes live!
ALA 2026 - Workshop at AAMAS 2026
Adaptive and Learning Agents (ALA) brings together researchers working on learning, adaptation, and autonomous behaviour in single- and multi-agent systems. The workshop welcomes contributions from across computer science (including reinforcement learning, agent architectures, evolutionary computation, planning, and game theory) as as well as from related fields such as cognitive science, biology, economics, and the social sciences.
ALA aims to foster collaboration, highlight recent advances, and provide a representative overview of current research on adaptive and learning agents. It serves as an inclusive forum for discussing both theoretical foundations and practical applications, spanning topics such as learning and adaptation in dynamic or open-ended environments, coordination and communication among multiple agents, incentive and mechanism design, and the emergence of collective behaviour in complex systems.
The workshop places particular emphasis on emerging learning paradigms and on methods that enable agents to operate reliably in large-scale, uncertain, or evolving environments. We encourage work that extends established techniques or introduces new frameworks to address the challenges of real-world adaptive and multi-agent systems. Topics of interest include, but are not limited to:
- Reinforcement learning (single- and multi-agent)
- Representation learning for single- and multi-agent systems
- Adaptation in dynamic environments
- Foundation models for adaptive (multi-)agent systems
- Multi-objective optimisation in single- and multi-agent systems
- Model-based RL and planning with learned world models (single- and multi-agent)
- Batch and offline (multi-agent) reinforcement learning
- Integrating learning with symbolic or game-theoretic reasoning
- Game theoretical analysis of adaptive multi-agent systems
- Neurosymbolic and logical reasoning for (multi-agent) decision-making
- Safety, robustness, and trustworthy (multi-agent) reinforcement learning
- Decentralized, federated, and communication-aware multi-agent learning
- Evolutionary and open-ended learning in multi-agent populations
- Co-evolution of agents in a multi-agent setting
- Cooperative exploration and learning to cooperate and collaborate
- Learning and modelling trust, reputation, and social norms in human–AI and multi-agent systems
- Emergent behaviour in adaptive multi-agent systems
- Multi-agent reinforcement learning and control for cyber-physical systems and robotics
- Self-organizing, swarm, and bio-inspired adaptive multi-agent systems
- Human-in-the-loop learning systems
- Applications of adaptive and learning agents and multi-agent systems to real world complex systems
Important Dates
Submission Details
Papers can be submitted through OpenReview.
We invite submission of original work, up to 8 pages in length (excluding references) in the ACM proceedings format (i.e., following the AAMAS formatting instructions). This includes work that has been accepted as a poster/extended abstract at AAMAS 2026. Papers are limited to 8 pages plus references. Additionally, we welcome submission of preliminary results, i.e. work-in-progress, as well as visionary outlook papers that lay out directions for future research in a specific area, both up to 6 pages in length, although shorter papers are very much welcome, and will not be judged differently. Finally, we also accept recently published journal papers in the form of a 2 page abstract.
Furthermore, for submissions that were rejected or accepted as extended abstracts at AAMAS, authors need to also append the received reviews and a pdfdiff.
All submissions will be peer-reviewed (double-blind). Accepted work will be allocated time for poster and possibly oral presentation during the workshop. In line with AAMAS, the workshop will be in person.
Program Committee
TBA.Organization
This year's workshop is organised by:- A. Alp Aydeniz (Oregon State University, US)
- Montaser Mohammedalamen (University of Alberta, CA)
- Xue Yang University of Galway, Ireland
- Florent Delgrange (Vrije Universiteit Brussel, BE)
- Enda Howley (University of Galway, IE)
- Daniel Kudenko (Leibniz University Hannover, DE)
- Patrick Mannion (University of Galway, IE)
- Ann Nowé (Vrije Universiteit Brussel, BE)
- Sandip Sen (University of Tulsa, US)
- Peter Stone (University of Texas at Austin, US)
- Matthew Taylor (University of Alberta, CA)
- Kagan Tumer (Oregon State University, US)
- Karl Tuyls (University of Liverpool, UK)