AAMAS.2027

@aamasconf.bsky.social

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26th International Conference on Autonomous Agents and Multiagent Systems May 3-7, 2027 Hanoi, Vietnam https://warwick.ac.uk/fac/sci/dcs/aamas2027/

ABSTRACT: As AI systems become increasingly autonomous and embedded in socio-technical environments, balancing innovation with social responsibility grows increasingly urgent. Multi-agent systems and autonomous agents offer valuable insights into decision-making, coordination, and adaptability, yet their deployment raises critical ethical and governance challenges. How can we ensure that AI aligns with human values, operates transparently, and remains accountable within complex social and economic ecosystems? This talk explores the intersection of AI ethics, governance, and agent-based perspectives, drawing on my work in AI policy and governance, as well as prior research on agents, agent organizations, formal models, and decision-making frameworks. Recent advancements are reshaping AI not just as a technology but as a socio-technical process that functions in dynamic, multi-stakeholder environments. As such, addressing accountability, normative reasoning, and value alignment requires a multidisciplinary approach. A central focus of this talk is the role of governance structures, regulatory mechanisms, and institutional oversight in ensuring AI remains both trustworthy and adaptable. Drawing on recent AI policy research, I will examine strategies for embedding ethical constraints in AI design, the role of explainability in agent decision-making, and how multi-agent coordination informs regulatory compliance. Rather than viewing regulation as a barrier, will show that responsible governance is an enabler of sustainable innovation, driving public trust, business differentiation, and long-term technological progress. By integrating insights from agent-based modeling, AI policy frameworks, and governance strategies, this talk underscores the importance of designing AI systems that are both socially responsible and technically robust. Ultimately, ensuring AI serves the common good requires a multidisciplinary approach—one that combines formal models, ethical consideratio…
ABSTRACT: Long-term autonomy refers to the ability of intelligent agents to operate independently for extended periods, adapting to changing environments, managing limited resources, and maintaining reliable performance with minimal human oversight. This talk presents a series of research contributions from my lab that address the fundamental challenges of long-term autonomy. These include metareasoning mechanisms for managing deliberation before action; concurrent reasoning and execution for responsiveness in real time; planning and acting under uncertainty and partial observability; operating in complex open-world settings with an unbounded number of other agents; learning self-competence models to guide action selection and determine when to seek assistance; and techniques for aligning agent behavior with human values and objectives through rich, interpretable feedback such as demonstrations and explanations. Ultimately, the objective of long-term autonomy is not to eliminate human involvement but to enable scalable and cost-effective collaboration between humans and intelligent agents.
An empty conference room, 1960s retrofuturism vibe. Arranged slightly off center a metal cart, on which a soda can and a fake plant are positioned.