Understanding Targets as Complex Adaptive Systems: A Rigorous Computational Framework for Dynamic Composition and Aggregation Under Uncertainty

Abstract

The overall objective of this project is to formulate an overarching computational framework to model targets as complex adaptive systems (CAS), utilizing information from various intelligence, surveillance and reconnaissance (ISR) sources. This framework will provide a rigorous axiomatic system for integrating information into the models through the process of aggregation and composition. In order to provide realistic analysis and prediction, the framework will tackle the challenges of dynamism, incompleteness, emergence, uncertainty and time constraints commonly associated with real world systems and information. In existing attempts to model targets as systems, one critical aspect that has not been clearly addressed involves the underlying mechanism for integrating the numerous “pieces” and “parts” that make up the target. Combining pieces is the process of aggregation and must handle inconsistencies among the pieces. Combining parts is the process of composition in which the parts are encapsulations of information with a set of meaningful operations defined on them. Parts are functional in nature and thus are driven by function composition. Extant research has not directly addressed this resulting in mathematically ad-hoc models opaque to analysis. We propose to develop a singular, rigorous, comprehensive computation framework that is axiomatic and provides the capabilities needed to model targets as systems based on a new model of complex adaptive Bayesian Knowledge Bases and a novel, powerful analytical framework capable of wholistic end-to-end quantitative analysis of performance, robustness, vulnerability, and impacts of change on our targets being modeled. Furthermore, our results will have significant impact beyond the DoD and is applicable to numerous domains of public purpose from crisis and catastrophe management for natural disasters and disease outbreaks to assessing the well-being of our financial system and national infrastructures.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 08, 2016
Source ID
N002441510046

Entities

People

  • Eugene Santos

Organizations

  • Board of Trustees of Dartmouth College
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy