4 research outputs found
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U.S. Government has
been making a concerted effort to ensure American leadership in AI. These broad
strategy documents have influenced organizations such as the United States
Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative
between the DAF and MIT to bridge the gap between AI researchers and DAF
mission requirements. Several projects supported by the DAF-MIT AI Accelerator
are developing public challenge problems that address numerous Federal AI
research priorities. These challenges target priorities by making large,
AI-ready datasets publicly available, incentivizing open-source solutions, and
creating a demand signal for dual use technologies that can stimulate further
research. In this article, we describe these public challenges being developed
and how their application contributes to scientific advances
Multi-view decision processes: The helper-AI problem
\ua9 2017 Neural information processing systems foundation. All rights reserved. We consider a two-player sequential game in which agents have the same reward function but may disagree on the transition probabilities of an underlying Markovian model of the world. By committing to play a specific policy, the agent with the correct model can steer the behavior of the other agent, and seek to improve utility. We model this setting as a multi-view decision process, which we use to formally analyze the positive effect of steering policies. Furthermore, we develop an algorithm for computing the agents\u27 achievable joint policy, and we experimentally show that it can lead to a large utility increase when the agents\u27 models diverge