6 papers
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…
On Generating Monolithic and Model Reconciling Explanations in Probabilistic Scenarios
Stylianos Loukas Vasileiou, William Yeoh, Alessandro Previti +1
Explanation generation frameworks aim to make AI systems' decisions transparent and understandable to human users. However, generating explanations in uncertain environments charac…
TRACE-CS: A Hybrid Logic-LLM System for Explainable Course Scheduling
Stylianos Loukas Vasileiou, William Yeoh
We present TRACE-CS, a novel hybrid system that combines symbolic reasoning with large language models (LLMs)to address contrastive queries in course scheduling problems. TRACE-CS…
A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
Antonio Rago, Stylianos Loukas Vasileiou, Francesca Toni +2
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-wor…
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…
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…