Conference Information
AAMAS 2026: International Joint Conference on Autonomous Agents and Multi-agent Systems
https://cyprusconferences.org/aamas2026/Submission Date: |
2025-10-01 |
Notification Date: |
2025-12-22 |
Conference Date: |
2026-05-25 |
Location: |
Paphos, Cyprus |
Years: |
25 |
CCF: b CORE: a* QUALIS: a1 Viewed: 63512 Tracked: 119 Attend: 22
Call For Papers
Areas of Interest We welcome the submission of technical papers describing significant and original research on all aspects of the theory and practice of autonomous agents and multiagent systems. If you are new to this community, then we encourage you to consult the proceedings of previous editions of the conference to fully appreciate the scope of AAMAS. At the time of submission, you will be asked to associate your paper with one of the following areas of interest: Learning and Adaptation (LEARN) Generative and Agentic AI (GAAI) Game Theory and Economic Paradigms (GTEP) Coordination, Organizations, Institutions, Norms, and Ethics (COINE) Search, Optimization, Planning, and Scheduling (SOPS) Representation, and Reasoning (RR) Engineering and Analysis of Multiagent Systems (EMAS) Modeling and Simulation of Societies (SIM) Human-Agent Interaction (HAI) Robotics and Control (ROBOT) Innovative Applications (IA) Additionally, AAMAS 2026 includes several special tracks. You can find more information about these tracks under the Calls menu item. Learning and Adaptation (LEARN) Area Chairs: Bo An, Vincent Corruble, Yali Du, Ferdinando Fioretto, Sarah Keren, Yasser Mohammad, Francisco Cruz Naranjo, Tan Minh Nguyen, Karthik Sankararaman, and Kun Shao Topics: Reasoning and learning under uncertainty Supervised learning Unsupervised and representation learning Reinforcement learning Multiagent learning Evolutionary and biologically inspired algorithms Learning agent capabilities Learning agent-to-agent interactions Human-in-the-loop learning Few-shot learning Distributionally-robust learning Adversarial learning Description: Papers in this area focus on aspects of single agent and multiagent learning and communication. This includes all areas of machine learning such as unsupervised, supervised, and reinforcement learning and methods such as evolutionary and adversarial approaches. Note that there is also an area on Generative Agents and Agentic AI which is described below. Generative and Agentic AI (GAAI) Area Chairs: Prashant Doshi, Kate Larson, and Karl Tuyls Topics: Agency and learning in LLMs Learning for value alignment Reinforcement learning from human feedback (RLHF) Planning or learning for agentic processes/workflows Agentic decision aids Verification and safety of LLMs/agentic systems Modeling and analysis of generative AI agents Multi-agent training of LLM agents (Multi-turn) Instruction following of agents Evaluation of LLMs/agentic systems Cooperative and coordination of generative agents Description: This area focuses on advancing agentic AI, exploring how generative agent models learn, align with human values, and interact effectively, both with humans and with other agents. Key themes include enabling agency in LLMs and other generative models through methods like reinforcement learning from human feedback, designing agents for complex workflows, and decision-making while emphasizing critical aspects like verification, safety, multi-agent training, agent-to-agent communication and evaluation of these systems. Game Theory and Economic Paradigms (GTEP) Area Chairs: Niclas Boehmer, Noam Hazon, Omer Lev, Miming Li, Shuai Li, Neeldhara Misra, Svetlana Obraztsova, Maria Silvia Pini, and Alan Tsang Topics: Auctions and Mechanism Design Bargaining and Negotiation Behavioural Game Theory Evolutionary Game Theory Non-Cooperative Games: Equilibrium Concepts Non-Cooperative Games: Computational Issues Non-Cooperative Games: Theory and Applications Voting and Preference Aggregation Social Choice and Social Networks Judgment Aggregation Fair Allocation Tournaments Matching Digital and Liquid Democracy Coalition Formation Cooperative Games Description: This area encompasses research on cooperative and non-cooperative games, social choice, and mechanism design. It is particularly interested in various computational aspects such as novel algorithms, equilibrium computation, and efficient outcomes. The area welcomes both theoretical explorations and analysis related to game theory, mechanism and market design, and social choice; as well as showcasing practical applications of these topics. Coordination, Organizations, Institutions, Norms, and Ethics (COINE) Area Chairs: Nirav Ajmeri, Reyhan Aydoğan, and Jaime Simão Sichman Topics: Coordination and teamwork Social network analysis Norms and normative systems Organizations and institutions Non-strategic coalition or team formation Communication, including using natural language Policy, regulation, and accountability Safety, robustness, trust, and reputation Ethical considerations, including bias, equity, fairness, privacy, safety, security, transparency Values and preferences Agreement Technologies: Negotiation and Argumentation Responsible socio-technical systems Explainability and interpretability of norms and ethics in human-agent teams Ethical challenges of using LLMs for coordination and cooperation Description: Research in agent and multiagent systems has a long history of developing techniques that balance agent autonomy, adaptation, and distributed social reasoning with system-level considerations such as organizational and institutional policy enforcement addressing safety, security and fairness considerations. Teamwork and human-machine cooperation has an increased relevance with the transformation of our societies into socio-technical systems. We need to ensure transparency, foster trust, and align social reasoning with societal norms and expectations. We also need to ensure human-machine and machine-machine cooperation is fostered responsibly, within an adequate accountability system and in alignment with the ethical values of individuals concerned. We encourage the submission of papers that highlight the design, development, evaluation, simulation, and analysis of novel, innovative, and impactful research on issues related to the above topics. Search, Optimization, Planning, and Scheduling (SOPS) Area Chairs: Roman Barták, Sara Bernardini, and Filippo Bistaffa Topics: Single-agent planning and scheduling Multi-agent planning and scheduling Decentralized planning and scheduling Planning under uncertainty Combinatorial optimization Constraint programming Distributed constraint reasoning Resource and task allocation Non-strategic coalition formation Plan and goal recognition Human-aware planning and scheduling Knowledge Representation/Engineering for SOPS Description: This area includes theoretical or experimental contributions to search, optimization, planning, and scheduling in single- and multi-agent systems. Important subfields include decentralized planning, planning under uncertainty, combinatorial optimization, distributed constraint reasoning, resource and task allocation, and non-strategic coalition formation. Approaches for planning and scheduling that explore the use of machine learning and/or foundation models in conjunction with classical techniques are encouraged. Likewise, all approaches to single- and multi-agent planning, including motion and path planning, and their interplay with other agent components, are relevant. Representation and Reasoning (RR) Area Chairs: Roberta Calegari, Brian Logan, Vanina Martinez and Valentina Tamma Topics: Neurosymbolic approaches Argumentation Agent theories and models Explainability Logics for agent reasoning Ontologies for agents Reasoning about knowledge, beliefs, goals, actions, plans, and change in multiagent systems Reasoning and problem solving in agent-based systems Verification of agents and multiagent systems Description: This area includes theoretical or experimental contributions to knowledge representation and reasoning in single-agent and multi-agent systems. Knowledge representation is to be interpreted broadly, encompassing formal approaches—such as epistemic, strategic, and description logics—as well as data-driven techniques like representation learning. We also welcome contributions involving semantic frameworks such as knowledge graphs and ontologies, particularly where they support agent interoperability, communication, or reasoning. Representation and reasoning in complex settings often require the integration of perception and sensing, enabling agents to operate effectively in dynamic and uncertain environments. Relevant reasoning paradigms include automated reasoning, theorem proving, verification-based approaches, probabilistic inference, and neurosymbolic methods, provided they are applied to or inspired by reasoning in agent and multi-agent systems. Special emphasis is placed on methods that promote trustworthiness and responsible AI. This includes techniques for managing uncertainty and bias in representations, ensuring accountability and transparency, and embedding fairness and explainability into decision-making processes. Engineering and Analysis of Multiagent Systems (EMAS) Area Chairs: Leandro Buss Becker, Angelo Ferrando and Zahia Guessoum Topics: Requirements capture & formal specification of multi-agent systems Programming paradigms & languages for autonomous agents Runtime infrastructures and deployment platforms for scalable MAS (cloud, edge, and hybrid) Continuous verification, validation & certification pipelines Testing, debugging and DevOps for large-scale agent ensembles Scalability, fault-tolerance & performance engineering of MAS platforms Engineering self-adaptive agents: lifecycle management, continuous evolution, and deployment pipelines Interoperability, business agreements & agent-to-agent protocols Declarative, logic-based and BDI agent programming & architectures Engineering MAS-based simulations for rigorous analysis & experimentation Sociotechnical governance tools for norms, ethics & accountability Human-centred engineering for usability, transparency & explainability Open-source toolchains, benchmarks & reproducible MAS testbeds Engineering learning agents: platform design, online adaptation Engineering MAS with LLM methods Hybrid symbolic-subsymbolic agent systems: engineering architectures and platforms for neurosymbolic reasoning Benchmarks, evaluation methodologies, and reproducible workflows for MAS Data-driven engineering processes for design, testing, and adaptation of agent systems Engineering MAS-based autonomous systems Description: This area invites contributions that advance the engineering of agents and multi-agent systems through general-purpose software abstractions, programming paradigms, methodologies, and system infrastructures. We welcome work spanning the full software engineering lifecycle—from requirements capture and formal specification to testing, validation, deployment, and evolution. Emphasis is placed on engineering-oriented approaches that support the development of robust, scalable, and maintainable agent-based systems, including those integrating symbolic reasoning and learning capabilities (e.g., neurosymbolic agents, reinforcement learning agents, or LLM-based agents), provided the focus remains on engineering challenges and solutions. Contributions that demonstrate the impact of these approaches in concrete application domains or in synergy with emerging technologies are also encouraged. Modeling and Simulation of (Artificial) Societies (SIM) Area Chairs: Shah Jamal Alam, Franziska Klügl and Fabian Lorig Topics: Modeling for agent-based simulation Simulation of complex systems Modeling of societies and social simulations Public policies and evaluation Simulation techniques, tools and platforms Analysis of agent-based simulations Calibration, Verification and Validation of agent-based simulation systems Robustness, Reliability and Trustworthiness of agent-based simulations Design and implementation of large-scale simulation High-performance computing and frameworks Interactive and participatory simulation LLM-based simulation Description: Artificial societies are computer simulations or models that are created to emulate and research the behavior of intricate social systems. These societies simulate the interactions and dynamics of people, animals or other entities to understand how individual behaviors lead to emergent structures and interactions. Agent-based models of artificial society provide a way to analyze the impact of regulations, incentives and other interventions that help to understand the complex dynamics of society as a whole. The area aims to find efficient solutions to model and simulate complex societal systems using agents-based models. Important application areas include ecology, biology, economics, transportation, management, organizational, and social sciences in general. In these areas, agent theories, metaphors, models, analysis, experimental designs, empirical studies, and methodological principles, all converge into simulation as a way of achieving explanations and predictions, exploration and testing of hypotheses, and better system designs. Human-Agent Interaction (HAI) Area Chairs: Beatrice Biancardi, Brittany Duncan, and Matthias Scheutz Topics: Human-agent interaction Agent-based analysis of human interactions Socially interactive agents Trust and explainability in human-agent interactions Human-robot interaction and collaboration Social robotics and social interactions Mixed-initiative and shared autonomy in human-agent interactions Groups of humans and agents Agent models and architectures for interaction with humans Design for human-agent interaction Virtual humans Description: Human interaction with artificially intelligent agents is becoming more commonplace, as such, developing and evaluating agents that can understand humans’ dynamics to support competent interaction. Significant challenges arise when transitioning from pure multiagent systems to hybrid systems that need to incorporate bi-directional human and agent interactions and sustain different competitive or collaborative situations. Agents need new models and architectures to better address the interaction with humans, including perception and recognition of humans’ internal states and activities at different levels, interaction modalities that support true coordination, and incorporation of human factors and ethics concerns. The design of human-agent interaction needs special concerns that combine requirements from the perspectives of both the agents and the humans. The interactive behavior of such agents can be inspired by human-human interaction and can, additionally, be embodied by virtual agents or robots to scaffold intuitive interaction. Robotics and Control (ROBOT) Area Chairs: Nicola Basilico and Maria Gini Topics: Coordination and collaboration in robotic systems Swarm and multi-robot collective behavior Robots in adversarial settings Perception and vision Networked systems and distributed robotics Foundation models for robotic agents Knowledge representation and reasoning in robotic systems Robot planning Mapping, localization and navigation Manipulation Robot control Robot learning Long-term (or lifelong) autonomy for robotic systems Execution monitoring and failure recovery for robots Robot modeling and simulation Explainability, trust and ethics for robots Description: Robots are embodied and situated agents with a deep connection to the physical world. Research on autonomous agents and multi-agent systems shares many challenges and synergies with robotics. We invite submissions that offer theoretical or applied contributions to agent-based approaches for single-robot and multi-robot systems, with particular emphasis on problems and solutions motivated by realistic scenarios typically encountered in robotic applications. All papers at the intersection of robotics and artificial intelligence, especially those grounded in agent research, fall within the scope of the Robotics and Control area of interest at AAMAS. Innovative Applications (IA) Area Chairs: Zehong (Jimmy) Cao and Gauthier Picard Topics: Deployed or emerging applications of agent-based systems Realistic agent-based models of human organizations Evaluation of the cognitive capabilities of agent-based systems Integrated applications of agent-based and other technologies Challenges and best practices of real-world deployments of agent-based technologies Description: The innovative applications area aims to showcase successful applications and novel uses of agent-based technologies. We encourage research on emerging areas of agent-based applications with measurable benefits, on various topics such as (but not limited to) social good, sustainability, and ethical AI. The innovative applications area is keen to attract research that is not only triggered by real-world applications, but provides realistic beneficial solutions for these applications. Collaborations with relevant stakeholders is highly valued, as it helps demonstrate the feasibility and impact of the work.
Last updated by Dou Sun in 2025-06-29
Acceptance Ratio
Year | Submitted | Accepted | Accepted(%) |
---|---|---|---|
2010 | 685 | 163 | 23.8% |
2009 | 651 | 132 | 20.3% |
2008 | 721 | 141 | 19.6% |
2007 | 531 | 121 | 22.8% |
2006 | 550 | 127 | 23.1% |
2005 | 530 | 130 | 24.5% |
2004 | 577 | 142 | 24.6% |
2003 | 466 | 115 | 24.7% |
2002 | 530 | 142 | 26.8% |
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Related Journals
CCF | Full Name | Impact Factor | Publisher | ISSN |
---|---|---|---|---|
b | The Journal of Systems Architecture: Embedded Software Design | 3.8 | Elsevier | 1383-7621 |
a | IEEE Journal on Selected Areas in Communications | 13.80 | IEEE | 0733-8716 |
c | Journal of Global Information Technology Management | 3.000 | Taylor & Francis | 1097-198X |
Journal of Cybersecurity and Privacy | MDPI | 2624-800X | ||
Advances in Decision Sciences | Hindawi | 2090-3359 | ||
International Journal of Critical Infrastructure Protection | 4.100 | Elsevier | 1874-5482 | |
IETE Technical Review | 2.500 | Taylor & Francis | 0256-4602 | |
b | Knowledge and Information Systems | 2.500 | Springer | 0219-1377 |
Journal of Construction Engineering | Hindawi | 2356-7295 | ||
c | Behaviour & Information Technology | 2.900 | Taylor & Francis | 0144-929X |
Full Name | Impact Factor | Publisher |
---|---|---|
The Journal of Systems Architecture: Embedded Software Design | 3.8 | Elsevier |
IEEE Journal on Selected Areas in Communications | 13.80 | IEEE |
Journal of Global Information Technology Management | 3.000 | Taylor & Francis |
Journal of Cybersecurity and Privacy | MDPI | |
Advances in Decision Sciences | Hindawi | |
International Journal of Critical Infrastructure Protection | 4.100 | Elsevier |
IETE Technical Review | 2.500 | Taylor & Francis |
Knowledge and Information Systems | 2.500 | Springer |
Journal of Construction Engineering | Hindawi | |
Behaviour & Information Technology | 2.900 | Taylor & Francis |
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