Players are roleplaying ICE raids in and to prepare for real-world situations. SteveTheGamer55 is live on YouTube . He's streaming a session to his 4.6 million subscribers of, a mod that allows people to role-play with other players. "Really wanna show you guys some real-life scenarios," he says, offering a little background on his character, a man headed to his job while on a work visa. His character doesn't get far before an SUV swings onto the sidewalk in front of him; masked ICE agents spill out of the vehicle.

On a crisp Saturday in March, beneath a canopy of pixelated cherry blossoms, two avatars stood in front of a digital altar crafted from shimmering quartz blocks and flickering redstone torches. They were surrounded by a sprawling Minecraft village, complete with custom-coded NPCs reciting lore about the couple's decade-long digital courtship. Nearby, pixelated foxes darted between guests--each one logged in from across the world, dressed in custom skins as forest druids and rogue mages. After the vows (typed and read aloud on Discord), guests dispersed for side quests, scavenger hunts, and an enchanted maze culminating in a virtual fireworks show. This wasn't a rehearsal for an in-person wedding--this was the wedding.

Creating robots that can perform general-purpose service tasks in a human-populated environment has been a longstanding grand challenge for AI and Robotics research. One particularly valuable skill that is relevant to a wide variety of tasks is the ability to locate and retrieve objects upon request. This paper models this skill as a Scavenger Hunt (SH) game, which we formulate as a variation of the NP-hard stochastic traveling purchaser problem. In this problem, the goal is to find a set of objects as quickly as possible, given probability distributions of where they may be found. We investigate the performance of several solution algorithms for the SH problem, both in simulation and on a real mobile robot. We use Reinforcement Learning (RL) to train an agent to plan a minimal cost path, and show that the RL agent can outperform a range of heuristic algorithms, achieving near optimal performance. In order to stimulate research on this problem, we introduce a publicly available software stack and associated website that enable users to upload scavenger hunts which robots can download, perform, and learn from to continually improve their performance on future hunts.

The Fourteenth Annual AAAI Mobile Robot Competition and Exhibition was held at the National Conference on Artificial Intelligence in Pittsburgh, Pennsylvania, in July 2005. This year marked a change in the venue format from a conference hall to a hotel, which changed how the robot event was run. As a result, the robots were much more visible to the attendees of the AAAI conference than in previous years. This allowed teams that focused on human-robot interaction to have many more opportunities to interact with people. This article describes the events that were held at the conference, including the Scavenger Hunt, Open Interaction, Robot Challenge, and Robot Exhibition.

The Fifteenth Annual AAAI Robot Competition and Exhibition was held at the National Conference on Artificial Intelligence in Boston, Massachusetts, in July 2006. This article describes the events that were held at the conference, including the Scavenger Hunt, Human Robot Interaction, and Robot Exhibition. The robot competition and exhibition has a long tradition of demonstrating innovative research in robotics (Rybski et al. 2006, Smart et al. 2005, Balch and Yanco 2005). From new approaches to canonical robotics problems to groundbreaking research in emerging areas, the robot program provides a forum for a diverse range of projects in mobile robotics. Recent years have witnessed a rise in the accessibility of mobile robot platforms with reasonably capable platforms being available for relatively low cost (Dodds and Tribblehorn 2006, Dodds et al. 2004) and not requiring a substantial effort to build hardware (Veloso et al. 2006) or software (Blank et al. 2003, Touretzky and Tira-Thompson 2005) architectures.

Scavenger hunts have existed for generations. I've written an app for Amazon's Alexa platform that modernizes scavenger hunts. The premise of the game doesn't change: find ten random items located in your home -- or around your neighborhood -- within an hour. Except that it is Alexa who facilitates the game play. My app is free to enable if you're one of the millions of people who own an Amazon Alexa.

Some argue that robots do not have free will and therefore cannot be held morally accountable for their actions. But UW psychologists are finding that people don't have such a clear-cut view of humanoid robots. The researchers' latest results show that humans apply a moderate amount of morality and other human characteristics to robots that are equipped with social capabilities and are capable of harming humans. In this case, the harm was financial, not life-threatening. But it still demonstrated how humans react to robot errors.

As SAILORS returns for the second summer, the new campers are giddy with excitement. After grabbing breakfast and getting to know one another, the girls situate themselves in a lecture room in the Gates Computer Science building at Stanford University. Professor Fei-Fei Li, director of the SAILORS program and the AI Lab as a whole, warmly welcomes the campers to the summer program, imparting the grounds on which the idea of an all-girls, two-week research-intensive program came about just two years ago. Though Professor Li acknowledges the recent talk of the possibility of AI becoming the "terminator next door" that some critics of the field fear, that was exactly what swayed her, along with co-director Olga Russakovsky, to feel the desperate need of bringing more females into the field of AI. Because, as Prof. Li puts it, when we have women who gravitate AI towards humanity–women who are compassionate, who care about AI safety–the potential benefits from the societal impact far outweigh the prospect of AI coming to dominate the world.

The Fifteenth Annual AAAI Robot Competition and Exhibition was held at the Twenty-First National Conference on Artificial Intelligence in Boston, Massachusetts, in July 2006. This article describes the events that were held at the conference, including the Scavenger Hunt, Human Robot Interaction, and Robot Exhibition.

Two overarching goals were promoted for the 2005 Mobile Robot Competition. The first was to give the competitions an exhibitionstyle format to make them as accessible to different areas of research as possible. This was change would place the competitions and exhibitions demonstrated at the Fourteenth Annual AAAI directly in line with the conference, Mobile Robot Competition and Exhibition, an teams would need to handle the challenges involved event hosted at the Twentieth National Conference with noisy, cluttered, and unstructured on Artificial Intelligence (AAAI 2005). The robot event had a particularly strong human environments. Scavenger Hunt: Autonomous robots were required to search a cluttered and crowded environment This year, AAAI changed the venue format for a defined list of objects and were from a convention center to a hotel setting. The Scavenger as defined by the team, and feedback Hunt event was organized by Douglas from the participants. Blank from Bryn Mawr College, the Robot Robot Challenge: Robots were required to attend Challenge and the Open Interaction Task were the conference autonomously, including organized by Ashley Stroupe from the Jet registering for the conference, navigating the Propulsion Laboratory, the research component conference hall, talking with attendees, and of the exhibition was organized by Magdalena answering questions.