Artificial Intelligence in the Military

Presented by

Carson English, Jason Lukis,

Nathan Morse and Nathan Swanson

Overview

• History

• Neural Networks

• Automated Target Discrimination

• Tomahawk Missile Navigation

• Ethical issues

History

• 1918 – first tests on guided missiles

• 1945 – Germany makes first ballistic missile

• 1950 – AIM-7 Sparrow– “fire-and-forget

History

• 1973 – remotely piloted vehicles (RPVs)– Used to confuse enemy air defenses

• 1983 – tomahawk missile first used by navy– Uses terrain contour matching system

• 1983 – Reagan make his famous star wars speech• 1988 – U.S.S. Vincennes mistakenly destroys

Iranian airbus due to autonomous friend/foe radar system

History

• 1991 – Smart bombs used in Gulf War to selectively destroy enemy targets– Praised for its precision and effectiveness

Neural Networks

• Inspired by studies of the brain

• Massively parallel

• Highly connected

• Many simple units

Structure of a neuron in a neural net

Neural net with three neuron layers

Three Main Neural Net Types

• Perceptron

• Multi-Layer-Perceptron

• Backpropagation Net

Perceptron

Multi-Layer-Perceptron

Backpropagation Net

·   pattern association ·   pattern classification ·   regularity detection ·   image processing ·   speech analysis ·   optimization problems ·   robot steering ·   processing of inaccurate or incomplete inputs ·   quality assurance ·   simulation

Areas where neural nets are useful

• the operational problem encountered when attempting to simulate the parallelism of neural networks

• inability to explain any results that they obtain

Limits to Neural Networks

Automated Target Discrimination

• SAR (Synthetic Aperture Radar)

• CFAR (Constant False Alarm Rate)

• QGD (Quadratic Gamma discriminator)

• NL-QGD (multi-layer perceptron)

• Example

• Results

As researched by the Computational NeuroEngineering Laboratory in Gainsville, FL

Synthetic Aperture Radar

• Data collection for ATD

• Self-illuminating imaging radar

• Creates a height map of a surface

• Maintains spatial resolution regardless of distance from target

• Can be used day and night regardless of cloud cover

Picture of SAR rendering

Two Constant False Alarm method for determining targets

Quadratic Gamma discrimination

Non Linear QGD

Example

Results

• After training, all three discriminators were run on a data set representing 7km2 of terrain. Target detection threshold was set to 100%.

• CAFR resulted in 4,455 false alarms.

• QGD resulted in 385 false alrams.

• NL-QGD resulted in 232 false alarms.

Tomahawk Missile Navigation

• Missile contains a map of terrain

• Figures out its current position from percepts (radar & altimeter)

• Uses a modified Gaussian least square differential correction algorithm, a step size limitation filter, and a radial basis function

Radial Basis Function

Gaussian Least Square Correction

Necessary Condition

Sufficient Condition

Step size limitation filter

Weight matrix

Tolerence error = 10^-8

Ethics

• Accountability– Legal– Political– Example: Aegis defense system shoots down an Iranian

Airbus jetliner in 1988

• Use of AI in warfare• Ethics of Research and Development

– Potential uses– Military Funding of AI– Passing of the blame “just doing my job”

Sources

• “Target Discrimination in Synthetic Aperture Radar (SAR) using Artificial Neural Networks” Jose C. Principe, Munchurl Kim, John W. Fisher III. Computational NeuroEngineering Laboratory. EB-486 Electrical and Computer Engineering Department. University of Florida.

• Sandia National Laboratories. http://www.sandia.gov/radar/sar.html

• Jet Propulsion Laboratory: California Institute of Technology. http://southport.jpl.nasa.gov/desc/imagingradarv3.html

• Wageningen University, The Netherlands. http://www.gis.wau.nl/sar/sig/sar_intr.htm