8 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder
Andreas Sauter, Saber Salehkaleybar, Frank van Harmelen +2
Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challenging due to causal ambiguity, rest…
Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives
Mohammadhossein Khojasteh, Yifan Jiang, Stefano De Giorgis +2
Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cogn…
Successful Misunderstandings: Learning to Coordinate Without Being Understood
Nikolaos Kondylidis, Anil Yaman, Frank van Harmelen +2
The main approach to evaluating communication is by assessing how well it facilitates coordination. If two or more individuals can coordinate through communication, it is generally…
"Stop replacing salt with sugar!'': Towards Intuitive Human-Agent Teaching
Nikolaos Kondylidis, Andrea Rafanelli, Ilaria Tiddi +2
Humans quickly learn new concepts from a small number of examples. Replicating this capacity with Artificial Intelligence (AI) systems has proven to be challenging. When it comes t…
Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
Qiyuan Li, Haijiang Liu, Caicai Guo +6
The large-scale development of large language models (LLMs) in medical contexts, such as diagnostic assistance and treatment recommendations, necessitates that these models possess…