Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming
Sabanci University（萨班哲大学）
Aysu Bogatarkan, Esra Erdem
备注：
Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 16 pages, 6 figures
The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming. This paper is under consideration for acceptance in TPLP.
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
Zhejiang University（浙江大学）；Ant Group（蚂蚁集团）；Alibaba-Zhejiang University Joint Institute of Frontier Technology（阿里巴巴-浙江大学前沿技术联合研究中心）；Zhejiang University-Ant Group Joint Laboratory of Knowledge Graph（浙江大学-蚂蚁集团知识图谱联合实验室）
Experimenting with recursive queries in database and logic programming systems
Università della Calabria（卡拉布里亚大学）
本文考虑了大量（可能是分布式的）数据的推理问题。目前，现有的建议显示出一些局限性：{ \em（i）}由于推理通常在主存中进行的事实，可以同时处理的数据的量是有限的; { \em（ii）}与外部的交互（和独立的）DBMS不是微不足道的，在某些情况下，根本不允许;{ \em（iii）}当前实现的效率对于它们在涉及大量数据的复杂推理任务中的利用仍然是不够的。本文提供了在这种情况下的贡献，它提出了一个新的系统，称为DLV $^{DB}$，旨在解决这些问题。此外，本文报告了一个彻底的实验分析的结果，我们已经进行了比较我们的系统与几个国家的最先进的系统（包括逻辑和数据库）的一些经典的演绎问题;其他测试系统是：LDL++，XSB，Smodels和三个顶级的商业DBMS。DLV $^{DB}$在递归查询上的性能甚至明显优于商业数据库系统。逻辑程序设计理论与实践（Theory and Practice of Logic Programming，TPLP）
A Survey on Music Generation from Single-Modal, Cross-Modal, and Multi-Modal Perspectives
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Innovation Center of Yangtze River Delta, Zhejiang University（浙江大学长三角创新中心）
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；ZJU-Ant Group Joint Lab of Knowledge Graph（浙江大学-蚂蚁集团知识图谱联合实验室）；Ant Group（蚂蚁集团）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Big Data Intelligent Computing（浙江省大数据智能计算重点实验室）
impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction
Center for Applied AI, Skolkovo Institute of Science and Technology, Moscow, Russian Federation（应用人工智能中心，斯克尔科夫科学与技术研究所，莫斯科，俄罗斯联邦）；BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory, University of Sharjah, Sharjah, United Arab Emirates（BIMAI实验室，生物医学导向人工智能实验室，肖贾联合大学，肖贾，阿拉伯联合酋长国）；Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russian Federation（伊万诺夫俄罗斯科学院系统编程研究所，莫斯科，俄罗斯联邦）
