Battle Swarm: An
Evolutionary Approach to
Complex Swarm Intelligence
Russel Ahmed Apu
Marina Gavrilova
Brief Outline
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Battle Swarms
Tactics and battle efficiency
Swarm Intelligence: Missile Genotype Encoding
Evolutionary strategies for battle swarms
Experimental results and analysis
Objective:
To utilize swarm based tactics & evolutionary
swarm strategies to increase tactical efficiency for
offensive and defensive agents
Battle Swarms Agents
• MISSILES
• DEFENSE TURRETS
• Autonomous
• Limited sensory
capabilities
• Limited intelligence
• Single objective: Hit ship
• Complex dynamic system
• Behavior of one missile
effect other missiles in the
swarm
• Point Defense system
• Only Visual/radar
capabilities
• Limited coverage
• Single Objective: Destroy
missiles
• Simple rule, complex
outcome: Select and fire
• Behavior and efficiency
cruicial to survival
• Fixed Strategy
• Evolutionary Strategy
Missile
Reaction
Reaction
Radius
Missile
Missile
Point
Defense
Defense Mechanism
Actions of a Missile
Action Encoding Set: {LRUDNMFAXYZ}*
X=Rand
L= Roll Left
F=Follow* Z=Diverge*
R=Roll Right
U=Pitch Up
N=NOP
D=Pitch Down
Y=Converge*
M=Memory A=Avoid*
PITCH
UP
Up
ROLL RIGHT
Constant
Thrust
Heading
Right
* Discussed in the next few slides
ROLL LEFT
PITCH
DOWN
Basic Sensory Encoding and Actions
Follow Target
COG
Proj(u)
(2)
Proj(v)
Pitch up
(1)
Projection Plane
Roll to match
proj(v)=proj(u)
Basic Sensory Encoding and Actions
(2)
Avoid Target
Pitch up
COG
Proj(u)
Proj(v)
(1)
Projection Plane
Roll to match
proj(v).proj(u)=0
Target Relative Coordinate
(Range, Heading, Bearing)
Range, R = M p - T
r
Fm = fx , fy , fz = Forward Vector of missile m
r
um = Up vector of missile m
é1- ( fx )2
-fx fy
-fx fz ù pm = Location of the missile m
ê
ú
ê
ú T = Location of the Target
2
1- ( fx )
-fy fz ú
M= ê -fx fy
ê
ú
2ú
ê -f f
(
)
f
-f
f
1x
y z
êë x z
ú
û
r
Heading, h = Fm · ( Mpm )
r
r
Bearing, b = um · (MT - pm ) ×((um ´ (MT - pm )) · Fm )
Target relative: Heading
Target Relative: Bearing
COG
u
Rproj
b
F
Projection Plane
PRproj
Swarm Relative Encoding
• Regulates the probability of Flocking Tendency
• ‘Y’ Flock and increase tendency (probability  of
Boids flocking)
• ‘Z’ Diverge from flock and decrease tendency
• If an agents decides to flock (prob= ), the direction is
determined using modified BOIDS
Boids flocking: From left to right rules of cohesion, separation and alignment [2].
Decision Making
• Event related decision are made by the
swarm implicitly
• Avoiding Incoming fire: Ionization trail
gives negative pheromone to allow flocking
out of a region
• Finding Weakness in Defense: Combined
usage of flocking tendency, gas and
ionization pheromone trail
Basic Encoding of Missile genotype
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String of Possible Action (I.e [LYUXLY])
Action string is circular (iterative)
Missile DNA=Gene_String[]
Continuous execution of the string
Each action executed for an infinite time
Regulates Swarm Behavior/Tendency
Encoding Basic Maneuvers
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Maintain Current Heading = [N]
Homing the Target = [F]
Ring Motion = [U]
Cork Screw = [LU], [LUMMM]
Evasive Approach = [XF], [XMMMF]
Basic Evasive Action = [A], [AMMMX]
Fall Back = [XU], [XMMMU], [AU]
Scramble = [X]
Basic Maneuvers
[F]
[XF]
[U]
[N]
[LU]
[A]
Different Complex Maneuvering
Tactics
• Retaliation – frontal attack
• Evasive – avoid fire at all costs
• Convergent approach – approach target from a
particular direction
• Divergent approach – surround and approach from
different directions
• Trail wind flocking –one missile leads others
• Distract and draw fire
Different Complex Maneuvering
Tactical strategies
(a) Diversion (b) Trail Wind Flocking
(c) Retaliation (d) Divergence
Mutating and Evolving the
Missile Genotype
• Fitness: Define a fitness function for the desired action
• Crossover: Augment/concatenate Genes
{[LUMU] [AMD]} {[LUAMDMU] [LUMD]
[AMDUMU] [LMDMU]…}
• Randomization: Replace arbitrary symbols with “X”…
run the simulation and convert meta genes to real genes
[FFLLU]  [FXLXU]  {[FULLU], [FFLFU],
[FNLNU], [FMLNU]}  Best{[FULLU], [FNLNU]}
Induced Evolution
• We can introduce certain desired behavior
in addition to natural evolution
• Step 1: Train Missiles separately to obtain
certain desired behavior without any other
consideration. Obtain Viral strain W=[…]
• Step 2: Infect All current Genotype with
viral Strain W (crossover)
The Game: Co-evolution
1. Implement basic missile [F] and basic Turret
{Select X, Fire@trajectory}
2. Adjust physical property to match
–
Fitness=50% (50% missiles hit the target)
3. Evolve Missiles and turrets against previous
strain
4. Repeat step 3 for several Games cycles
5. If fitness falls or rises dramatically increase
physical strength of opposing swarm (Missile:
Acceleration, velocity, turning. Turrets: Speed
of fire, number of turrets, firing frequency)
Wi =
The Fitness Function: Hetero-Sexual
Mating
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•, J
W
i
i
Use a two dimensional Fitness Function
Every missile has a masculine and a feminine fitness
Masculine: Ability to Attack
Feminine: Ability to Survive
W i = Wi , J
i
# of missile hit t ing t he t arget
Wi =
# of missiles launched
J
i
# of gunshot s successfully evaded
=
# of missiles dest royed+ 1
Results
- Strategies evolved, Runtime and
other aspects
Fitness Function
50
45
Feminine
40
35
Evasive
30
Dispersion
25
20
Distraction
15
Swarm:
Trail Wind
10
Assult
5
0
0
20
Masculine
40
60
Randomized
80
100
No Randomization
Complex Tactics: Convergent Approach
• High Efficiency
• Low evasion
• Highly Masculine
• Strength in numbers
• Less exposure to incoming
fire
• Increase of spatial threat
• Decrease of temporal threat
Complex Tactics: Divergent Approach
• Cause more distraction and
confuse the defense system
• Less likelihood for a missile
to draw fire
• Decrease of spatial threat
• Increase of temporal threat
• Lower Efficiency
• Highly Evasive
• Highly Feminine
Convergent VS Divergent
Approach
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CONVERGENT
Less defense turrets
Draw less fire
Easy to shoot down
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DIVERGENT
More defense turrets
Draw more fire
Distracting and hard to
shoot down
Complex Tactics: Trail Wind Flocking
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Better than “Convergent Approach”
Least exposure to incoming fire
Lot of opportunity for diversion/distraction
Decrease of spatial threat
Decrease of average temporal threat
More Results
(a) Funnel Shaped
Assault
(b) Parachute Phase 1:
Forming a moth ball
(c) Parachute Phase 2:
Dispersing
(d) Parachute Phase3:
Forming a Head
(e) Parachute Phase 4:
Trail Wind Attack
(f) Divergent Attack
More Results
Figure 11: Formation of Distraction, Organic and Deception pattern
(g) Distraction 1: Early
missiles draw fire
(h) Distraction 2: Assault
in progress
(i) Organic motion pattern
(j) Deception 1: Lead
Assault
(k) Deception 2:
Overshooting the target
(l) Deception 3: Come
about and attack
More Results
See Animation Demos
Rendering and Physical Engine
• Regular physics engine will not suffice
– Approximation aggravates trajectory
computation
• Construct everything from scratch
– Advanced look-ahead estimation based physics
engine
– Robust Rendering engine:
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Anisotropic Texture filtering
Multiple LOD based geometry rendering
Particle engine
Highly optimized exclusive API for performance
Flexibility
The Simulation Engine
• Robust design: Separation of Rendering
modules from the simulation
• Implements Command Console
• Runtime performance is highly efficient
• For 50 missiles:
– Full quality rendering@ 50FPS !!!
– Simulation runs upto 50 times faster
(FPS=2200+) is rendering is turned off (for
evolutionary algorithm)
– Excellent Rendering quality (anisotropic texture
mapping, particle engine)
Mode
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400
N
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100
St
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Frame/Second
Runtime Performance
Performance of Different Runtime Modes
800
715.73
700
600
500
391.74
300
200
110.33
48.77
0
Summary
• Using Swarm Intelligence to evolve battle tactics for
– Missiles
– Point Defense Turrets
• Evolutionary strategies:
– Gene_String[] evolution
– The novel “Induced Evolution” strategy
– Co-evolutionary strategy
• Implementation:
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Rendering and physical Engine
Genotype encoding
Basic maneuvers
Complex maneuvers
Integration
Thank you