Call for Papers

We invite you to submit your best work in the area of agents and multiagent systems to AAMAS-2024, the 23rd International Conference on Autonomous Agents and Multiagent Systems, to be held in Auckland in May 2024.

All submissions will be rigorously peer-reviewed and evaluated on the basis of the overall quality of their technical contribution, taking into account criteria such as originality, significance, soundness, reproducibility, clarity, relevance to the conference, quality of presentation, as well as understanding and appropriate referencing of the state of the art.

The papers will be published under CC BY licence. 

Important Dates

  • Abstract submission: 2 Oct 2023
  • Paper submission: 9 Oct 2023
  • Rebuttal period: 30 Nov – 4 Dec 2023
  • Author notification: 20 Dec 2023
  • Camera-ready paper: 8 Feb 2024
  • Conference: 6-10 May 2024

All deadlines are at the end of the specified day, anywhere on Earth (UTC-12).

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 11 areas of interest:

For submission instructions, please see here.

In addition, AAMAS 2024 also includes the following special tracks.

More information on these areas and the topics covered can be found here:

Coordination, Organisations, Institutions, Norms and Ethics

Area Chairs: Marija Slavkovik / Juan Carlos Nieves


  • Norms, Normative systems
  • Organizations and institutions
  • Policy, regulation, sanctions, accountability and legislation
  • Trust and reputation
  • Ethical values in multi-agent systems, including privacy, safety, security and transparency
  • Responsible socio-technical systems
  • Methodologies for the development of trustworthy AI
  • Trustworthy AI education within the scope of MAS

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 organisational and institutional policy enforcement addressing safety, security and fairness considerations.  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 ensure social reasoning conforms to societal norms and expectations. We also need to ensure human-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.

Engineering Multiagent Systems

Area Chairs: Matteo Baldoni / Amit Chopra


  • Requirements and formal specification
  • Architecture and modelling
  • Formal verification and validation
  • Programming models and languages
  • Testing, maintenance, and evolution
  • Concurrency, fault tolerance, robustness, performance and scalability
  • Sociotechnical systems, norms, and governance
  • Responsibility and accountability
  • Interoperability, business agreements, and interaction protocols
  • BDI-based agents
  • Engineering ethical agents
  • Tools and testbeds
  • Technological paradigms, including microservices, the Web, the IoT, Cloud computing, distributed Ledgers, and Robotics
  • Middleware and platforms for MAS
  • Engineering learning agents
  • Usability
  • Applications, including Finance, Health, Agriculture, Autonomous Vehicles and Smart-*

Description: This area invites contributions that focus on general-purpose software abstractions and methodologies (including software systems) that advance the engineering of agents and multiagent systems. Contributions that demonstrate the benefit of such abstractions and methodologies for interesting application domains and other technological paradigms are also welcome. Naturally, the scope of this area spans the entire software engineering lifecycle — from requirements and verification to testing, validation, and evolution.

Humans and AI / Human-Agent Interaction

Area Chairs: Rui Prada / Kary Främling


  • Human-agent interaction
  • Agent-based analysis of human interactions
  • Socially interactive agents
  • Trust and explainability in human-agent interactions
  • Mixed-initiative and shared autonomy in human-agent interactions
  • Groups of humans and agents
  • Agents models and architectures for interaction with humans
  • Designing for human-agent interaction
  • Virtual humans

Description: In a world where AI is increasingly prevalent and hybrid systems with humans and agents interacting becomes more frequent, it is crucial to study and create agents that can understand human social dynamics and have competent interaction with people. Significant challenges arise when transitioning from pure multiagent systems to hybrid systems that need to incorporate mixed-initiative from humans and agents, and sustain different competitive or collaborative social situations. Agents need new models and architectures to better address the interaction with people including, perception and recognition of human activities at different levels, interaction techniques that coordinate well with humans, and concerns for user experience and ethics, such as, trust and explainability. The design of human-agent interaction systems need special concerns that combine requirements from the perspectives of both the agents the humans.The creation of agents with such capabilities can be inspired by human-human interactions and can, additionally, be applied to simulations with virtual humans or support the analysis of data from human social interactions.

Innovative Applications

Area Chairs: Nardine Osman / Vicent Botti


  • Innovative applications of agent-based systems tackling SDGs and LNOB
  • Innovative applications of agent-based systems tackling issues in ethical AI
  • Realistic agent-based models of human organisations
  • Evaluation of the cognitive capabilities of agent-based systems
  • Integrated applications of agent-based and other technologies
  • Challenges and best practices of deploying agent-based technologies to real-world scenarios

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, focusing on topics such as social good, sustainability, and ethical AI. We invite research that addresses any of the United Nations Sustainable Development Goals (SDGs) ( or the Leave No One Behind Principle (LNOB) ( Given the extensive debate on ethical AI, we also strongly invite research addressing principles such as (but not limited to) beneficence, promotion of human well-being and flourishing, and ensuring AI’s alignment with human values. 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.

Knowledge Representation, Reasoning, and Planning

Area Chairs: Val Goranko / Wojtek Jamroga


  • Agent theories and models
  • Coalition formation (non-strategic)
  • Communication and argumentation
  • Distributed problem solving / constraint reasoning
  • Formal methods for cybersecurity
  • Logics for agent reasoning
  • Ontologies for agents
  • Single-agent and multi-agent planning and scheduling
  • Reasoning about action, plans and change in multi-agent systems
  • Reasoning about knowledge, beliefs, and norms in multi-agent systems
  • Reasoning about goals and strategies in multi-agent systems
  • Reasoning and problem solving in agent-based systems
  • Teamwork, team formation, teamwork analysis
  • Verification of multi-agent systems

Description: This area includes theoretical or experimental contributions to knowledge representation, reasoning and planning in single-agent and multi-agent systems. Knowledge representation is to be understood broadly, ranging from theoretical contributions (e.g., epistemic, strategic, description, and other logics) to ontologies and beyond. Relevant  forms of reasoning include, for instance, automated reasoning and theorem proving approaches, as well as verification-based approaches, as long as they are applied to, or motivated by reasoning about agents and/or multi-agent systems. Likewise, all approaches to single- and multi-agent search and planning – from motion planning to symbolic planning – and their interplay with other agent components are relevant. 

Learning and Adaptation

Area Chairs: Nils Jansen / Paulo Novais


  • (Adversarial) multi-agent systems
  • Markov decision processes
  • Reasoning and learning under uncertainty
  • Co-evolutionary algorithms
  • Machine learning and deep learning
  • Evolutionary algorithms
  • Learning agent capabilities
  • Learning agent-to-agent interactions
  • Tools & applications

Description: Autonomous Agents must sense, deliberate, decide, and act in potentially complex and uncertain environments. In addition, in many cases, they must interact with other agents and/or humans. Anticipating each situation and hardcoding the appropriate agent behavior becomes impossible as the complexity of the environment and interactions increase. As such, adaptivity and learning are key properties that imbue autonomy to agents operating in the real world. Papers in this area focus on all aspects of single agent and multiagent planning and learning.

Markets, Auctions, and Non-Cooperative Game Theory

Area Chairs: Paolo Turrini / Nicolas Troquard


  • 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
  • Practical Applications of Non-Cooperative Game Theory

Description: This area encompasses research on non-cooperative games, specifically focusing on computational aspects such as algorithmic and complexity analysis for equilibrium computation and verification. The track also welcomes theoretical explorations and analysis related to non-cooperative games. In particular, it highlights the ramifications of non-cooperative game theory in various domains such as market and mechanism design, including auctions, bargaining, negotiation, behavioural and evolutionary game theory. Submissions showcasing practical applications of game theory are also strongly encouraged.


Modelling and Simulation of (Artificial) Societies

Area Chairs: Michael Lees / Harko Verhagen


  • Analysis of agent-based simulations
  • Calibration methods for socio-demographic data
  • Agent-based models & Social Networks
  • Applications of agent-based simulations in social phenomena (polarisation, inequality,etc.)
  • Emergent behaviour
  • Engineering agent-based simulations 
  • Interactive simulation
  • Modelling for agent-based simulation
  • Simulation of complex systems
  • Simulation techniques, tools and platforms
  • Social simulation
  • Validation of social simulation systems

Description: Artificial societies are computer simulations or models that are created to emulate and research the behaviour of intricate social systems. These societies simulate the interactions and dynamics of people, animals or other organisms to understand how individual behaviours lead to emergent structures and interactions. Agent-based models of artificial society provide a way to analyse 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, organisational, 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.


Social Choice and Cooperative Game Theory

Area Chairs: Ulle Endriss / Piotr Skowron


  • Voting and Preference Aggregation
  • Social Choice and Social Networks
  • Judgment Aggregation
  • Fair Allocation
  • Matching
  • Digital Democracy
  • Coalition Formation
  • Cooperative Games 

Description: This area covers all aspects of social choice theory, the study of the design and analysis of methods for collective decision making, including in particular voting and the fair allocation of resources. It also covers the theoretical, algorithmic, and practical aspects of coalition formation and cooperative game theory.

(Multi-agent) Reinforcement Learning

Area Chairs: Matthijs Spaan / Matt Taylor / Shuyue Hu


  • Single- and Multi-agent Reinforcement Learning (RL)
  • Markov Decision Processes
  • Sequential Decision Making
  • RL in partially observable settings
  • RL in adversarial settings
  • Model-based RL
  • Multi-armed Bandits
  • Imitation Learning, Inverse RL, and Learning from Demonstration
  • Transfer Learning, Lifelong Learning, and Continual Learning in RL settings
  • RL Theory
  • RL for Robotics
  • Human Interaction with RL Agents
  • Safe, Robust, Explainable RL
  • Neural Architectures for RL
  • Applications of RL

Description: For 2024, reinforcement learning will be a separate area from Learning and Adaptation. We welcome all work related to sequential decision making in reinforcement-learning settings. Theoretical, algorithmic, and practical aspects of reinforcement learning are all welcome, and a focus on how reinforcement learning agents interact with other agents or people are particularly welcome.


Area Chairs: Luca Iocchi / Joana Campos


  • Execution monitoring and Failure recovery for robots
  • Explainability, trust and ethics for robots
  • Human-robot interaction and collaboration
  • Knowledge representation and reasoning in robotic systems
  • Long-term (or lifelong) autonomy for robotic systems
  • Machine learning for robotics
  • Mapping, localisation and exploration
  • Mixed Human-Robot teams
  • Multi-robot coordination and collaboration 
  • Networked systems and distributed robotics
  • Robot control
  • Robots in adversarial settings
  • Social robotics and social interactions
  • Swarm and collective behaviour

Description: Robotics is one of the exciting fields in agent research. Both practical and analytical techniques in agent research influence, and are being influenced by, research in autonomous robots and multi-robot systems. We invite papers that advance theory and application of single and multiple robots, with particular emphasis on solutions based on realistic assumptions typically encountered in robotic applications. Papers on integrative research about the interaction between robots and agents (broadly defined) are particularly welcome, but all papers at the intersection of robotics and artificial intelligence (and agent research, specifically) are in the scope of the robotics area at AAMAS.

The reviewing process for each of these areas will be coordinated by dedicated area chairs familiar with the particularities of the area they are responsible for. We reserve the right to transfer a paper to a different area in case we believe that doing so will improve the quality of the reviewing process. You will have the opportunity to react to preliminary versions of the reviews of your paper (so-called “rebuttal”) before we take a final decision regarding the acceptance of your paper.

Special Tracks

In addition to the main track, AAMAS-2024 will feature three special tracks, the Blue Sky Ideas Track, the JAAMAS Track, and the Demo Track, each with a separate Call for Papers. There will also be a fast track for AAAI 2024 papers rejected with positive reviews.

The focus of the Blue Sky Ideas Track is on visionary ideas, long-term challenges, new research opportunities, and controversial debate. The JAAMAS Track offers authors of papers recently published in the Journal of Autonomous Agents and Multiagent Systems (JAAMAS) that have not previously appeared as full papers in an archival conference the opportunity to present their work at AAMAS-2024. The Demo Track, finally, allows participants from both academia and industry to showcase their latest developments in agent-based and robotic systems.

Organising Committee

AAMAS 2024 General Chairs:

Mehdi Dastani (Utrecht University, Netherlands)

Jaime Simão Sichman (University of São Paulo, Brazil)

AAMAS 2024 Program Chairs:

Natasha Alechina (Utrecht University, Netherlands)

Virginia Dignum (Umeå University, Sweden)

AAMAS 2024 Local Chairs:

Jiamou Liu (University of Auckland, New Zealand)

Yang Chen (University of Auckland, New Zealand)

Tony Savarimuthu (University of Otago, New Zealand)

Kaiqi Zhao (University of Auckland, New Zealand)