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Battle Swarm: An Evolutionary Approach to Complex Swarm Intelligence Russel Ahmed Apu Marina Gavrilova.

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Presentation on theme: "Battle Swarm: An Evolutionary Approach to Complex Swarm Intelligence Russel Ahmed Apu Marina Gavrilova."— Presentation transcript:

1 Battle Swarm: An Evolutionary Approach to Complex Swarm Intelligence Russel Ahmed Apu Marina Gavrilova

2 Brief Outline Battle Swarms Tactics and battle efficiency Swarm Intelligence: Missile Genotype Encoding Evolutionary strategies for battle swarms Experimental results and analysis

3 Objective: To utilize swarm based tactics & evolutionary swarm strategies to increase tactical efficiency for offensive and defensive agents

4 Battle Swarms Agents MISSILES Autonomous Limited sensory capabilities Limited intelligence Single objective: Hit ship Complex dynamic system Behavior of one missile effect other missiles in the swarm Evolutionary Strategy DEFENSE TURRETS 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

5 Defense Mechanism Missile Point Defense Reaction Radius Reaction

6 Actions of a Missile Constant Thrust Right Up Heading ROLL LEFT ROLL RIGHT PITCH DOWN Action Encoding Set: {LRUDNMFAXYZ}* X=Rand Y=Converge* L= Roll Left U=Pitch Up N=NOP F=Follow* Z=Diverge* R=Roll Right D=Pitch Down M=Memory A=Avoid* * Discussed in the next few slides PITCH UP

7 Basic Sensory Encoding and Actions COG Follow Target Projection Plane (1) Roll to match proj(v)=proj(u) (2) Pitch up Proj(v) Proj(u)

8 Basic Sensory Encoding and Actions COG Avoid Target Projection Plane (1) Roll to match proj(v).proj(u)=0 (2) Pitch up Proj(v) Proj(u)

9 Target Relative Coordinate (Range, Heading, Bearing)

10 Target relative: Heading

11 Target Relative: Bearing COG Projection Plane u PR proj b R proj F

12 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].

13 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

14 Basic Encoding of Missile genotype 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

15 Encoding Basic Maneuvers 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]

16 Basic Maneuvers [N] [A] [U] [F] [XF] [LU]

17 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

18 Different Complex Maneuvering Tactical strategies (a)Diversion (b) Trail Wind Flocking (c) Retaliation (d) Divergence

19 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]}

20 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)

21 The Game: Co-evolution 1.Implement basic missile [F] and basic Turret {Select X, 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)

22 The Fitness Function: Hetero-Sexual Mating Use a two dimensional Fitness Function Every missile has a masculine and a feminine fitness Masculine: Ability to Attack Feminine: Ability to Survive

23 Results - Strategies evolved, Runtime and other aspects

24 Fitness Function Masculine Feminine Randomized No Randomization Evasive Dispersion Distraction Swarm: Trail Wind Assult

25 Complex Tactics: Convergent Approach Strength in numbers Less exposure to incoming fire Increase of spatial threat Decrease of temporal threat High Efficiency Low evasion Highly Masculine

26 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

27 Convergent VS Divergent Approach CONVERGENT Less defense turrets Draw less fire Easy to shoot down DIVERGENT More defense turrets Draw more fire Distracting and hard to shoot down

28 Complex Tactics: Trail Wind Flocking Better than “Convergent Approach” Least exposure to incoming fire Lot of opportunity for diversion/distraction Decrease of spatial threat Decrease of average temporal threat

29 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

30 More Results Figure 11: Formation of Distraction, Organic and Deception pattern (h) Distraction 2: Assault in progress (g) Distraction 1: Early missiles draw fire (i) Organic motion pattern (j) Deception 1: Lead Assault (k) Deception 2: Overshooting the target (l) Deception 3: Come about and attack

31 More Results See Animation Demos

32 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: Anisotropic Texture filtering Multiple LOD based geometry rendering Particle engine Highly optimized exclusive API for performance Flexibility

33 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 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)

34 Runtime Performance

35 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: –Rendering and physical Engine –Genotype encoding –Basic maneuvers –Complex maneuvers –Integration

36 Thank you


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