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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…
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…
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…
Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach
Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh +2
In today's competitive financial landscape, understanding and anticipating customer goals is crucial for institutions to deliver a personalized and optimized user experience. This…
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…
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…