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60 Artificial Intelligence for the Internet of Everything

The final step in controlling swarms is to embrace the heterogeneity of plat-
forms and sensors that we can employ and let agents specialize to their suited
tasks while still retaining a degree of anonymity (Kott & Abdelzaher, 2014).
A team that includes multiple warfighters and multiple artificial agents
must be capable of distributed learning and reasoning. Besides distributed
learning, these include such challenges as: multiple decentralized mission-
level task allocations; self-organization, adaptation, and collaboration; space
management operations; and joint sensing and perception. Commercial
efforts to date have been largely limited to single platforms in benign settings.
Military-focused programs, like Micro Autonomous Systems and Technol-
ogy and Collaborative Technology Alliance (MAST CTA) (Piekarski et al.,
2017), have been developing collaborative behaviors for UAVs. Ground
vehicle collaboration is challenging and is largely still at the basic research
level at present. In particular, to address such challenges, a new collaborative
research alliance called Distributed and Collaborative Intelligent Systems
and Technology (DCIST) has been initiated (https://dcist-cra.org/). Note
that the battlefield environment imposes yet another complication: because
the enemy interferes with communications, all of this collaborative, distrib-
uted AI must work well even with limited, intermittent connectivity.

3.7 HUMANS IN THE OCEAN OF THINGS
In this vision of the future warfare, a key challenge is to enable autonomous
systems and intelligent agents to effectively and naturally interact across a
broad range of warfighting functions. Human-agent collaboration is an
active ongoing research area that must address such issues as trust and trans-
parency, common understanding of shared perceptions, and human-agent
dialog and collaboration (Kott & Alberts, 2017).
One seemingly relevant technology is question answering—the system’s
ability to respond with relevant, correct information to a clearly stated
question. Successes of question-answering commercial technologies are
indisputable. They work well for very large, stable, and fairly accurate
volumes of data (e.g., encyclopedias). But such tools do not work for rapidly
changing battlefield data, which is also distorted by an adversary’s conceal-
ment and deception. They cannot support continuous, meaningful dialog in
which both warfighters and AI agents develop a shared situational awareness
and intent understanding. Research is being performed to develop human-
robotic dialog technology for warfighting tasks using natural voice, which is
critical for reliable battlefield teaming.