6 papers · 1 filter
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…
Inferring Implicit Goals Across Differing Task Models
Silvia Tulli, Stylianos Loukas Vasileiou, Mohamed Chetouani +1
One of the significant challenges to generating value-aligned behavior is to not only account for the specified user objectives but also any implicit or unspecified user requiremen…
Human-Aware Belief Revision: A Cognitively Inspired Framework for Explanation-Guided Revision of Human Models
Stylianos Loukas Vasileiou, William Yeoh
Traditional belief revision frameworks often rely on the principle of minimalism, which advocates minimal changes to existing beliefs. However, research in human cognition suggests…
Dialectical Reconciliation via Structured Argumentative Dialogues
Stylianos Loukas Vasileiou, Ashwin Kumar, William Yeoh +2
We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured…