Pixel Matrices and Other Compositional Analyses of Interconnected Systems

Abstract

The new world of interconnected-everything brings new challenges to those who wishto understand it and keep society safe from unintended and unimagined consequences.With constant communication and feedback loops being the norm, the space of behaviorsis too large to analyze by simulation alone. Today’s nearly unlimited computational powermust be used more wisely, so that our knowledge of a system can evolve along with thesystem itself. New mathematical techniques are needed to provide the algebraic formulasfor combining our insights, just as we combine components, allowing us to anticipate thebehavior of an assembled system. Category theory is the mathematics of combination andcompositionality, so it is well-suited as a foundation for such work.We propose to investigate compositional techniques for analyzing systems of all sorts.At the mathematical center of many disciplines, one needs to solve a system of simultaneousequations. As mundane, abstract, and worked-over as this may seem, a new elementarytechnique was recently discovered with the potential to change how we approach suchproblems. This technique is highly compositional—the solutions to subsystems can becombined to form a solution of the whole—and it emerged out of a similarly compositionalapproach to understanding the behavior of networked machines. Just as circuits can becombined to form computers, machines of all scales can be interconnected to form morecomplex machines. The common theme is compositionality: whether combining theconstraints and requirements necessary to design a robot, or combining the equations thatdescribe its function, our goal is to find analyses that are scalable and reusable, so that theknowledge we gain today is efficiently utilized in the networks of tomorrow.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 06, 2017
Source ID
FA95501710058

Entities

People

  • David Spivak

Organizations

  • Air Force Office of Scientific Research
  • Harvard University
  • United States Air Force

Tags

Readers

  • Data Mining and Knowledge Discovery.
  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space