Evidence map›Paper›PMID 35444590›Full record

ArticleFrontiers in psychology2022

The Future of Collaborative Human-Artificial Intelligence Decision-Making for Mission Planning.

Sue E Kase, Chou P Hung, Tomer Krayzman, James Z Hare, B Christopher Rinderspacher, Simon M Su

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sue E KaseU.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States.
Chou P HungU.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States.
Tomer KrayzmanU.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States.
James Z HareU.S. Army Combat Capabilities Development Command - Army Research Laboratory, Adelphi, MD, United States.
B Christopher RinderspacherU.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States.
Simon M SuU.S. Army Combat Capabilities Development Command - Army Research Laboratory, Aberdeen Proving Ground, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an increasingly complex military operating environment, next generation wargaming platforms can reduce risk, decrease operating costs, and improve overall outcomes. Novel Artificial Intelligence (AI) enabled wargaming approaches, based on software platforms with multimodal interaction and visualization capacity, are essential to provide the decision-making flexibility and adaptability required to meet current and emerging realities of warfighting. We highlight three areas of development for future warfighter-machine interfaces: AI-directed decisional guidance, computationally informed decision-making, and realistic representations of decision spaces. Progress in these areas will enable development of effective human-AI collaborative decision-making, to meet the increasing scale and complexity of today's battlespace.

Indexed as

artificial intelligenceAugmented/mixed realitydecision-makinginterfacevisualizationwargaming and wargames

Identifiers

PMID35444590
PMCPMC9014866

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.