The system behaves less like a gamble and more like a prediction engine — one whose true product is not wagers, but ...

Adapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialog systems. We adopted the approach of treating explanation generation as a non-stationary ...

Abstract: Since the introduction of Dynamic Bayesian Networks (DBNs), their efficiency and effectiveness have increased through the development of three significant aspects: (i) modeling, (ii) ...

Fifteen RCTs with 3,400 participants were included. Exercise was linked to small-to-moderate improvements in general (SMD = 0.59), fluid (SMD = 0.43), and crystallized intelligence (SMD = 0.64).

The neural networks dominating AI in recent years have achieved a remarkable level of behavioral flexibility, in part due to their capacity to learn new tasks from only a few examples. These ...

Continual learning (CL), the ability to learn new tasks without forgetting existing ones, is one of the greatest challenges in AI. Our work provides an analytically tractable theory that captures some ...

Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reflecting the goodness-of- fit of the structure ...