A critical factor in utilizing agents with Artificial Intelligence (AI) is
their robustness to novelty. AI agents include models that are either
engineered or trained. Engineered models include knowledge of those aspects of
the environment that are known and considered important by the engineers.
Learned models form embeddings of aspects of the environment based on
connections made through the training data. In operation, however, a rich
environment is likely to present challenges not seen in training sets or
accounted for in engineered models. Worse still, adversarial environments are
subject to change by opponents. A program at the Defense Advanced Research
Project Agency (DARPA) seeks to develop the science necessary to develop and
evaluate agents that are robust to novelty. This capability will be required,
before AI has the role envisioned within mission critical environments. As part
of the Science of AI and Learning for Open-world Novelty (SAIL-ON), we are
mapping possible military domain novelty types to a domain-independent ontology
developed as part of a theory of novelty. Characterizing the possible space of
novelty mathematically and ontologically will allow us to experiment with agent
designs that are coming from the DARPA SAIL-ON program in relevant military
environments. Utilizing the same techniques as being used in laboratory
experiments, we will be able to measure agent ability to detect, characterize,
and accommodate novelty.

研究 AI 代理人的鲁棒性来应对新奇环境，DARPA 的 SAIL-ON 计划致力于开发必要的科学来评估代理人的鲁棒性，同时利用实验室实验中使用的技术来衡量代理人检测、表征和适应新奇性的能力。

军事领域中的新颖性特征

Characterizing Novelty in the Military Domain

The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in
artificial intelligence (AI) systems so that they are capable of learning (and
improving) continuously, leveraging data on one task to improve performance on
another, and doing so in a computationally sustainable way. Performers on this
program developed systems capable of performing a diverse range of functions,
including autonomous driving, real-time strategy, and drone simulation. These
systems featured a diverse range of characteristics (e.g., task structure,
lifetime duration), and an immediate challenge faced by the program's testing
and evaluation team was measuring system performance across these different
settings. This document, developed in close collaboration with DARPA and the
program performers, outlines a formalism for constructing and characterizing
the performance of agents performing lifelong learning scenarios.

DARPA 的 L2M 项目旨在开发能够进行终身学习并能提升的人工智能系统，文献中提出了一个形式化构建和表征代理的性能的方法，以应对该项目中多样化机器人的任务结构和持续时间。

终身学习指标

Lifelong Learning Metrics

A number of organizations ranging from terrorist groups such as ISIS to
politicians and nation states reportedly conduct explicit campaigns to
influence opinion on social media, posing a risk to democratic processes. There
is thus a growing need to identify and eliminate "influence bots" - realistic,
automated identities that illicitly shape discussion on sites like Twitter and
Facebook - before they get too influential. Spurred by such events, DARPA held
a 4-week competition in February/March 2015 in which multiple teams supported
by the DARPA Social Media in Strategic Communications program competed to
identify a set of previously identified "influence bots" serving as ground
truth on a specific topic within Twitter. Past work regarding influence bots
often has difficulty supporting claims about accuracy, since there is limited
ground truth (though some exceptions do exist [3,7]). However, with the
exception of [3], no past work has looked specifically at identifying influence
bots on a specific topic. This paper describes the DARPA Challenge and
describes the methods used by the three top-ranked teams.

为了识别和消除在社交媒体上操纵舆论的『操纵机器人』，DARPA 在 2015 年 2 月 / 3 月举办了一项竞赛，多个在 DARPA 社交媒体战略传播计划支持下的团队参赛，本文介绍了这次竞赛以及三个最高排名团队所使用的方法。