With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is an important paradigm for studying various aspects when humans and AI agents work together. The unique aspect of Human-AI teaming research is the need to jointly study humans and AI agents, demanding multidisciplinary research efforts from machine learning to human-computer interaction, robotics, cognitive science, neuroscience, psychology, social science, and complex systems. However, existing platforms for Human-AI teaming research are limited, often supporting oversimplified scenarios and a single task, or specifically focusing on either human-teaming research or multi-agent AI algorithms. We introduce CREW, a platform to facilitate Human-AI teaming research and engage collaborations from multiple scientific disciplines, with a strong emphasis on human involvement. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. Following conventional cognitive neuroscience research, CREW also supports multimodal human physiological signal recording for behavior analysis. Moreover, CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines. With CREW, we were able to conduct 50 human subject studies within a week to verify the effectiveness of our benchmark.

Programmers at OpenAI, an artificial intelligence research company, recently taught a gaggle of intelligent artificial agents -- bots -- to play hide-and-seek. Not because they cared who won: The goal was to observe how competition between hiders and seekers would drive the bots to find and use digital tools. The idea is familiar to anyone who's ever played the game in real life; it's a kind of scaled-down arms race. When your opponent adopts a strategy that works, you have to abandon what you were doing before and find a new, better plan. It's the rule that governs games from chess to StarCraft II; it's also an adaptation that seems likely to confer an evolutionary advantage.

Humans are a species that can adapt to environmental challenges, and over eons this has enabled us to biologically evolve -- an essential characteristic found in animals but absent in AI. Although machine learning has made remarkable progress in complex games such as Go and Dota 2, the skills mastered in these arenas do not necessarily generalize to practical applications in real-world scenarios. The goal for a growing number of researchers is to build a machine intelligence that behaves, learns and evolves more like humans. A new paper from San Francisco-based OpenAI proposes that training models in the children's game of hide-and-seek and pitting them against each other in tens of millions of contests results in the models automatically developing humanlike behaviors that increase their intelligence and improve subsequent performance. Hide-and-seek was selected as a fun starting point mostly due to its simple rules, says the paper's first author, OpenAI Researcher Bowen Baker.

Researchers at the OpenAI artificial intelligence laboratory developed bots that trained themselves to cooperate by playing hide-and-seek. Scientists at the OpenAI artificial intelligence (AI) laboratory have developed AI bots that trained themselves to cooperate by playing hide-and-seek. The team had the bots play the game in a simulated environment containing fixed walls and movable boxes; each bot had its own perspective of its surroundings, and could not directly communicate with other bots. The bots that hid quickly deduced the fastest way to fool seekers was to find objects in the environment with which to conceal themselves; the seekers learned they could manipulate objects like ramps to overcome obstacles like walls. The bots learned that cooperation--like passing objects to each other or co-building a hideout--was the quickest way to win.