DeepAI
Log In Sign Up

An Experimentation Platform for Explainable Coalition Situational Understanding

10/27/2020
by   Katie Barrett-Powell, et al.
0

We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing. The Situational Understanding Explorer (SUE) platform is designed to be lightweight, to easily facilitate experiments and demonstrations, and open. We discuss our requirements to support coalition multi-domain operations with emphasis on asset interoperability and ad hoc human-machine teaming in a dense urban terrain setting. We describe the interface functionality and give examples of SUE applied to coalition situational understanding tasks.

READ FULL TEXT

page 3

page 4

10/16/2019

Explainable AI for Intelligence Augmentation in Multi-Domain Operations

Central to the concept of multi-domain operations (MDO) is the utilizati...
07/01/2022

Shai-am: A Machine Learning Platform for Investment Strategies

The finance industry has adopted machine learning (ML) as a form of quan...
03/09/2022

Explainable Machine Learning for Predicting Homicide Clearance in the United States

Purpose: To explore the potential of Explainable Machine Learning in the...
06/22/2020

PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms

Operationalizing AI has become a major endeavor in both research and ind...
12/14/2021

Towards Explainable Artificial Intelligence in Banking and Financial Services

Artificial intelligence (AI) enables machines to learn from human experi...
06/21/2021

Trinity: A No-Code AI platform for complex spatial datasets

We present a no-code Artificial Intelligence (AI) platform called Trinit...
05/17/2021

Automated Biodesign Engineering by Abductive Meta-Interpretive Learning

The application of Artificial Intelligence (AI) to synthetic biology wil...