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20242026
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cs.AI2026

Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us

Stylianos Loukas Vasileiou, Antonio Rago, Francesca Toni +1

Computational argumentation offers formal frameworks for transparent, verifiable reasoning but has traditionally been limited by its reliance on domain-specific information and ext…

cs.AI2025

How Do People Revise Inconsistent Beliefs? Examining Belief Revision in Humans with User Studies

Stylianos Loukas Vasileiou, Antonio Rago, Maria Vanina Martinez +1

Understanding how humans revise their beliefs in light of new information is crucial for developing AI systems which can effectively model, and thus align with, human reasoning. Wh…

cs.AI2025

Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces

Yinxu Tang, Stylianos Loukas Vasileiou, William Yeoh

Explainable AI is increasingly employing argumentation methods to facilitate interactive explanations between AI agents and human users. While existing approaches typically rely on…

cs.AI2025

Explainable Distributed Constraint Optimization Problems

Ben Rachmut, Stylianos Loukas Vasileiou, Nimrod Meir Weinstein +2

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool to model cooperative multi-agent problems that need to be solved distributively. A core assump…

cs.AI2024

Approximating Human Models During Argumentation-based Dialogues

Yinxu Tang, Stylianos Loukas Vasileiou, William Yeoh

Explainable AI Planning (XAIP) aims to develop AI agents that can effectively explain their decisions and actions to human users, fostering trust and facilitating human-AI collabor…