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Marcin Waniek.  Towards a Science of Experimental Complexity: An Artificial-Life Approach to Modeling Warfare  Andy Ilachinski, Center for Naval Analyses.

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Presentation on theme: "Marcin Waniek.  Towards a Science of Experimental Complexity: An Artificial-Life Approach to Modeling Warfare  Andy Ilachinski, Center for Naval Analyses."— Presentation transcript:

1 Marcin Waniek

2  Towards a Science of Experimental Complexity: An Artificial-Life Approach to Modeling Warfare  Andy Ilachinski, Center for Naval Analyses

3  Homogeneous forces that are continually engaged in combat  Soldiers always aware of the position and condition of all opposing units  Appropriate for static trench warfare or artillery duels  Rather unrealistic for modern (and also much older) battlefield

4  "War is... not the action of a living force upon lifeless mass... but always the collision of two living forces.„ - Carl von Clausewitz  “The fight is chaotic yet one is not subject to chaos.” – Sun Tzu

5  Dynamical system composed of many nonlinearly interacting adaptive agents.  Local action, which often appears disordered, induces long range order.  No master “voice” that dictates the actions of each and every combatant.  Military forces must continually adapt to a changing combat environment.

6  Irreducible Semi-Autonomous Adaptive Combat  Bottom-up, synthesist approach to the modeling of combat.  „Conceptual playground" to explore high- level emergent behaviors arising from various low-level interaction rules.  Model patterned after mobile cellular automata rules.

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8  Doctrine: a default local-rule set specifying how to act in a generic environment  Mission: goals directing behavior  Situational Awareness: sensors generating an internal map of environment  Adaptability: an internal mechanism to alter behavior and/or rules

9  Agent belongs to one of two armies – Red or Blue  Agent exists in one of three states – alive, injured or dead  Each agent has defined sensor and weapon range  Each agent is equipped with personality defined by vector ω = (ω1, ω2,..., ω6) where -1 ≤ ωi ≤ 1 and |ω1| |ω6| = 1.

10  ω1 - the number of alive friendly agents  ω2 - the number of alive enemy agents  ω3 - the number of injured friendly agents  ω4 - the number of injured enemy agents  ω5 – the distance from friendly flag  ω6 – the distance from enemy flag

11  ω = (1/20, 5/20, 0, 9/20, 0, 5/20) five times more interested in moving toward alive enemies than alive friendlies, even more interested in moving toward injured enemies  ω = (-1/6,-1/6,-1/6,-1/6,-1/6,-1/6) wants to move away from, rather than toward, every other agent and both flags, i.e. it wants to avoid action of any kind.

12  Rules telling how to alter agents personality according to environmental conditions.  Basic meta-rule classes: advance toward enemy flag, cluster with friendly forces, engage the enemy in combat  Examples of other meta-rules: retreat, pursuit, support, hold position.

13  Red effectively encircles Blue forces  Fixed Blue personalities unable to find countermeasures

14  Example of non- monotonic behavior  Enlarging Red forces sensor range leads to a worse outcome

15  Red forces bred using genetic algorithm, Blue forces fixed  Red able to weaken the center of Blue line, and then attack the weak spot with all forces

16  Enhanced ISAAC Neural Simulation Toolkit  Context-dependent and user-defined agent behaviors (i.e. personality scripts)  On-line genetic algorithm, neural-net, reinforcement-learning, and pattern recognition toolkits  Agents fighting as a part of small units

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