A Neural-Symbolic Cognitive Architecture Framework for Handling Novel Entities in Open Worlds Background Traditional AI research assumes that intelligent agents operate in a “closed world”, where all task-relevant concepts in the environment are known, and no new unknown situations will arise. However, in the open real world, novel entities that vi...

Adapting to Open-World Events via Learning Spatio-Temporal Dynamics on Mobility Networks Research Background In modern society, the Mobility-as-a-Service (MaaS) system is seamlessly integrated by various transportation modes (such as public transport, ride-sharing, and shared bicycles). To achieve efficient MaaS operation, modeling the spatio-tempo...

There has long been a problem of “weak supervision” in the field of artificial intelligence, where only part of the labels are observable in the training data, while the remaining labels are unknown. Multiple Instance Learning (MIL) is a paradigm to address this issue. In MIL, the training data is divided into multiple “bags”, each containing multi...

Traditional representation learning methods usually design a fixed basis function architecture to achieve desired properties such as orthogonality and sparsity. In contrast, the idea of deep reinforcement learning is that the designer should not encode the properties of the representation, but instead let the data flow determine the properties of t...

In model-based fault diagnosis, the ability to identify the key observational data that leads to system abnormalities is highly valuable. This paper introduces a framework and algorithm for identifying key observational data. The framework determines which observations are crucial for diagnosis by abstracting the raw observational data into “sub-ob...

With the widespread application of graph-structured data in various fields, Graph Neural Networks (GNNs) have attracted significant attention as a powerful tool for analyzing graph data. However, existing GNNs primarily rely on neighborhood node similarity information when learning node representations, overlooking the potential of node differences...