ISAAC & EINSTein

ISAAC & EINSTeinMarcin Waniek

Towards a Science of Experimental Complexity: An Artificial-Life Approach to Modeling Warfare

Andy Ilachinski, Center for Naval AnalysesBased onLanchaster EquationsHomogeneous forces that are continually engaged in combatSoldiers always aware of the position and condition of all opposing unitsAppropriate for static trench warfare or artillery duelsRather unrealistic for modern (and also much older) battlefield

"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

But as wise people said

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.Land Combat as a Complex Adaptive SystemIrreducible Semi-Autonomous Adaptive CombatBottom-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.ISAAC

Conway's Game of LifeDoctrine: a default local-rule set specifying how to act in a generic environmentMission: goals directing behaviorSituational Awareness: sensors generating an internal map of environmentAdaptability: an internal mechanism to alter behavior and/or rulesISAAC agentAgent belongs to one of two armies Red or BlueAgent exists in one of three states alive, injured or deadEach agent has defined sensor and weapon rangeEach agent is equipped with personality defined by vector = (1, 2, ..., 6) where -1 i 1 and |1| + ... + |6| = 1.

ISAAC agent behavior1 - the number of alive friendly agents2 - the number of alive enemy agents3 - the number of injured friendly agents4 - the number of injured enemy agents5 the distance from friendly flag6 the distance from enemy flagPersonality vector = (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 = (-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.Personality examplesRules 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 combatExamples of other meta-rules: retreat, pursuit, support, hold position.Meta-RulesRed effectively encircles Blue forcesFixed Blue personalities unable to find countermeasuresSample #1

Example of non-monotonic behaviorEnlarging Red forces sensor range leads to a worse outcomeSample #2

Red forces bred using genetic algorithm, Blue forces fixedRed able to weaken the center of Blue line, and then attack the weak spot with all forcesSample #3

Enhanced ISAAC Neural Simulation ToolkitContext-dependent and user-defined agent behaviors (i.e. personality scripts)On-line genetic algorithm, neural-net, reinforcement-learning, and pattern recognition toolkitsAgents fighting as a part of small unitsEINSTeinThank you for your attention