The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that some recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment.

Machine learning can solve PR problems by tackling fake news and cutting through the clutter. A business needs to be noticed, in today's times, to attract more opportunities. PR makes an impressive social presence possible for any business. But PR is a tough nut to crack and experts are now using machine learning to solve PR problems. Before jumping to the PR problems, we should first understand what public relation (PR) is.

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that many recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on abstract PR instances. We also discuss further research necessary to tackle real-world pipe-routing instances of interest to industry today. This opens up a new direction of industrial relevance for the MAPF research community.

If the fragmented media environment is a sick patient, machine learning may be the cure. That was the proposition Andrew Heyward, visiting scholar from MIT's Media Laboratory and former president of CBS News, outlined in his presentation, "Can Robots Solve Your PR Problems?" at the New York offices of agency Makovsky on Feb. 6. Heyward and his colleagues at MIT Media Lab's Laboratory for Social Machines are studying artificial intelligence solutions to modern plights of the PR practitioner: fake news, polarization, the public's lack of faith in journalism and short attention spans, to name a few. Heyward's group uses machine learning algorithms as their primary tool to map and track the overall health of the public sphere. And soon, PR pros may be able to use those AI insights to make better decisions--whether they're managing a crisis or planning a national campaign. Here are four PR applications of AI and machine learning shared by Heyward.

Had artificial intelligence been named something less spooky, we'd probably worry about it less. Artificial intelligence, it seems, has a PR problem. While it's true that today's machines can credibly perform many tasks (playing chess, driving cars) that were once reserved for humans, that doesn't mean that the machines are growing more intelligent and ambitious. It just means they're doing what we built them to do. The robots may be coming, but they are not coming for us--because there is no "they."