In the multi-agent path-finding (MAPF) problem a plan is needed to move a set of agents from their initial location to their goals without collisions. In this paper we introduce and study the k-robust MAPF problem, where we seek a plan that is robust to k unexpected delays per agent.

In a multi-agent path-finding (MAPF) problem, the task is to find a plan for moving a set of agents from their initial locations to their goals without collisions. Following this plan, however, may not be possible due to unexpected events that delay some of the agents. Guaranteeing that collisions will never occur may be impossible. An important task is to find a plan that is very likely to succeed, even though unexpected delays may occur. We propose an algorithm for finding a plan in which the probability that no collisions will occur is at least a given parameter p (p-robust plan). We show that finding an optimal p-robust plan is significantly more difficult than finding an optimal standard plan. As a practical solution, we propose a greedy algorithm based on the Conflict-Based Search framework. Our experiments show that it finds p-robust plans with cost that is relatively close to the optimal cost of the standard, non-robust plans.

Multi-agent path finding (MAPF) is the problem of moving a set of agents from their individual start locations to their individual goal locations, without collisions. This problem has practical applications in video games, traffic control, robotics, and more. In MAPF we assume that agents occupy one location each time step. However, in real life some agents have different size or shape. Hence, a standard MAPF solution may be not suited in practice for some applications. In this paper, we describe a novel algorithm, based on the CBS algorithm, that finds a plan for moving a set of train-agents, i.e., agents that occupy a sequence of two or more locations, such as trains, buses, planes, or even snakes. We prove that our solution is optimal and show experimentally that indeed such a solution can be found. Finally, we explain how our solution can also apply to agents with any geometric shape.

In the Multi-Agent Meeting (MAM) problem, the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Search algorithm that finds the optimal meeting location under different cost functions. A number of admissible heuristics are proposed and experiments demonstrate the benefits of MM*.

Multi-Agent Path Finding (MAPF) is the problem of finding non-colliding paths for multiple agents. The classical problem assumes that all agents are homogeneous, with a fixed size and behavior. However, in reality agents are heterogeneous, with different sizes and behaviors. In this paper, we generalize MAPF to G-MAPF for the case of heterogeneous agents. We then show how two previous settings of large agents and k-robust agents are special cases of G-MAPF.

China leads the pack in the next healthcare breakthrough with the government and stalwart innovators bankrolling a big effort in artificial intelligence and big data that could essentially blaze a new trail in the sector, says an expert. "For years, the United States has been seen as the impeccable frontrunner for a big bang coming in the healthcare sector, in terms of money invested in digital healthcare, as well as the number of groundbreaking startups," said Sigal Atzmon, president of Medix Group, an international company that offers medical case management. "When it comes to the implementation of trailblazing technologies in the healthcare sector, however, China is the clear leader," Atzmon noted. Unlike the United States, where people have the "whole existing infrastructure legacy" that must be taken down and an awfully lot of stakeholders involved who may not favor a change, China has much more freedom to chart its own course and implement the game-changing technologies thanks to the "late-mover advantage", or how innovative newcomers outperform pioneers, she said. The Belgium-born senior corporate executive, who founded Medix in 2006 after her own scary experience with a breast mammogram, believes AI technology and digital healthcare stand as the unique and cost-effective answer for the country's under-doctored, underfunded and overburdened hospitals, where patients reportedly have to wait a long time or even pay large sums of money to middlemen to get appointments with renowned specialists.