Causal Adaptive Decision Aid (CADA)

Abstract

Logistics and planning personnel are overloaded by the increasing number of high dimensional data layers they are expected to analyze in short amounts of time (e.g., a 24-hr battle rhythm). The goal of the Causal Adaptive Decision Aid (CADA) is to facilitate the foraging and sensemaking steps of their analyses by rapidly summarizing these layers and displaying the cause-and-effect concepts which are relevant to recommend explainable courses of action (COAs). A causal (as opposed to correlation-based) model for COA recommendation has the benefits of being more robust to novel contexts by mitigating sample bias issues, while also playing a key role in analytic tradecraft. We achieve these goals by developing and delivering software modules related to the following four major tasks

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Document Details

Document Type
Technical Report
Publication Date
Jun 28, 2023
Accession Number
AD1204606

Entities

People

  • Aruna Jammalamadaka
  • Rajan Bhattacharyya

Organizations

  • HRL Laboratories

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Change Detection
  • Computational Science
  • Computer Vision
  • Contracts
  • Data Sets
  • Decision Support Systems
  • Eye Tracking
  • Failure Mode And Effect Analysis
  • Image Recognition
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Psychology
  • Reasoning

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Library and Information Science
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.