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ISAAC & EINSTein

ISAAC & EINSTein. Marcin Waniek. Based on. Towards a Science of Experimental Complexity : An Artificial-Life Approach to Modeling Warfare Andy Ilachinski , Center for Naval Analyses. Lanchaster Equations. H omogeneous forces that are continually engaged in combat

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ISAAC & EINSTein

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  1. ISAAC & EINSTein Marcin Waniek

  2. Based on • Towards a Science of Experimental Complexity:An Artificial-Life Approach to Modeling Warfare • Andy Ilachinski, Center for NavalAnalyses

  3. LanchasterEquations • Homogeneousforces that are continually engaged in combat • Soldiers always aware of the positionand condition of all opposing units • Appropriate for static trench warfare or artillery duels • Ratherunrealistic for modern (and also much older) battlefield

  4. But as wisepeoplesaid • "War is ... not the action of aliving 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. Land Combat as a Complex Adaptive System • Dynamicalsystem composed of many nonlinearly interacting adaptive agents. • Localaction, which often appears disordered, induces longrange order. • No master “voice” that dictates the actions of each and every combatant. • Military forces must continually adapt to a changing combat environment.

  6. ISAAC • IrreducibleSemi-AutonomousAdaptiveCombat • Bottom-up, synthesist approach to the modeling ofcombat. • „Conceptualplayground" to explore high-level emergent behaviors arising from various low-levelinteraction rules. • Model patternedafter mobile cellularautomatarules.

  7. Conway's Game of Life

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

  9. ISAAC agent behavior • Agent belongs to one of twoarmies – Red or Blue • Agent exists in one of threestates – alive, injuredordead • Each agent hasdefined sensor and weaponrange • Each agent isequipped withpersonalitydefined by vectorω = (ω1, ω2, ..., ω6)where -1 ≤ ωi ≤ 1 and |ω1| + ... + |ω6| = 1.

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

  11. Personalityexamples • ω = (1/20, 5/20, 0, 9/20, 0, 5/20)five times more interestedin moving toward aliveenemies than alive friendlies,evenmore 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, everyother agent and both flags,i.e. it wants to avoid action of any kind.

  12. Meta-Rules • Rules tellinghow to alter agents personality according to environmental conditions. • Basic meta-ruleclasses: advance toward enemy flag, cluster with friendly forces, engage theenemy in combat • Examples of other meta-rules:retreat, pursuit, support,hold position.

  13. Sample #1 • Red effectivelyencircles Blue forces • Fixed Blue personalitiesunable to findcountermeasures

  14. Sample #2 • Example of non-monotonicbehavior • Enlarging Red forces sensor rangeleads to a worseoutcome

  15. Sample #3 • Red forcesbredusinggeneticalgorithm, Blue forcesfixed • Red able to weaken the center of Blue line, and thenattack the weak spot with allforces

  16. EINSTein • Enhanced ISAAC Neural Simulation Toolkit • Context-dependent and user-definedagent behaviors (i.e. personality scripts) • On-linegeneticalgorithm, neural-net, reinforcement-learning, and pattern recognition toolkits • Agentsfighting as a part of small units

  17. Thankyou for yourattention

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