The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths
for multiple agents, where the key constraint is that the agents will be able to follow these …

Many mathematical models have been leveraged to design embeddings for representing
Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks …

Abstract Purpose of Review Planning collision-free paths for multiple robots is important for
real-world multi-robot systems and has been studied as an optimization problem on graphs …

Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of
robots is tasked with transporting a set of tasks, each from an initial location and each to a …

This paper proposes a new neural architecture for collaborative ranking with implicit
feedback. Our model, LRML (Latent Relational Metric Learning) is a novel metric learning …

The multi-agent path-finding (MAPF) problem has recently received a lot of attention.
However, it does not capture important characteristics of many real-world domains, such as …

近年来, 随着互联网技术和应用模式的迅猛发展, 引发了互联网数据规模的爆炸式增长,
其中包含大量有价值的知识. 如何组织和表达这些知识, 并对其进行深入计算和分析备受关注 …

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this
research area, numerous diverse search-based techniques were developed in the past 6 …

Since the beginning of the 21st century, the geoscience research has been entering a
significant transitional period with the establishment of a new knowledge system as the core …

Abstract Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents
such that each agent reaches its goal and the agents do not collide. In recent years, variants …