In this paper, we focus on a specific subclass of coordination problems in which humans are able to discover self-explaining deviations (SEDs).

In this paper, we focus on a specific subclass of coordination problems in which humans are able to discover self-explaining deviations (SEDs).

While many of these interactions happen through direct contact, like the concert-goers' nudging, some interactions can transmit through the material the objects are on or in -- these are known as indirect interactions. For example, a bridge with pedestrians on it can transmit vibrations, like in the famous Millennium Bridge "wobbly bridge" instance. While the results of direct interactions (like nudging) are of increasing interest and study, and the results of indirect interactions through mechanisms like vision are well-studied, researchers are still learning about indirect mechanical interactions (for example, how two rolling balls might influence each other's movement on a trampoline by indenting the trampoline's surface with their weight, thus exerting mechanical forces without touching). Physicists are using small wheeled robots to better understand these indirect mechanical interactions, how they play a role in active matter, and how we can control them. Their findings, "Field-mediated locomotor dynamics on highly deformable surfaces" are recently published in the The Proceedings of the National Academy of Sciences (PNAS).

Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world. In this paper, we focus on a specific subclass of coordination problems in which humans are able to discover self-explaining deviations (SEDs). SEDs are actions that deviate from the common understanding of what reasonable behavior would be in normal circumstances. They are taken with the intention of causing another agent or other agents to realize, using theory of mind, that the circumstance must be abnormal. We first motivate SED with a real world example and formalize its definition. Next, we introduce a novel algorithm, improvement maximizing self-explaining deviations (IMPROVISED), to perform SEDs. Lastly, we evaluate IMPROVISED both in an illustrative toy setting and the popular benchmark setting Hanabi, where it is the first method to produce so called finesse plays, which are regarded as one of the more iconic examples of human theory of mind.

Since the first Roomba came out in 2002, it has seemed inevitable that one day iRobot would develop a robotic lawnmower. After all, a robot mower is basically just a Roomba that works outside, right? Of course, it's not nearly that simple, as iRobot has spent the last decade or so discovering, but they've finally managed to pull it off. Today, iRobot is previewing its Terra robotic mower. It's rugged, fully autonomous, and literally wireless, using radio beacons to localize rather than relying on a buried edge wire to keep it from mulching your begonias.

About an hour after taking my iPhone X out of its box, I'm bouncing up and down as high as I can on a trampoline, trying to see how FaceID works under, let's say, less than ideal circumstances. I'm eight feet in the air, sweating and grimacing, only half-paying attention to the phone as I try not to break my legs on impact. I grab the phone, hold it up to my face, and try to focus. The first time, FaceID works great. It unlocks virtually instantly, as soon as I fix my eyes upon the TrueDepth camera in the notch around the iPhone X's screen.