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Tactical Event Resolution Using Software Agents, Crisp Rules, and a Genetic Algorithm

Tactical Event Resolution Using Software Agents, Crisp Rules, and a Genetic Algorithm. John M. D. Hill, Michael S. Miller, John Yen, and Udo W. Pooch Department of Computer Science Texas A&M University. Agenda. Tactical Event Resolution Design Architecture Genetic Component

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Tactical Event Resolution Using Software Agents, Crisp Rules, and a Genetic Algorithm

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  1. Tactical Event Resolution Using Software Agents, Crisp Rules, and a Genetic Algorithm John M. D. Hill, Michael S. Miller, John Yen, and Udo W. Pooch Department of Computer Science Texas A&M University

  2. Agenda • Tactical Event Resolution • Design • Architecture • Genetic Component • Rule-based Component • Results

  3. Tactical Event Resolution • Normally a manual, ad hoc, process where the forces and combat effects on each side are tallied and the Operations officer and the Intelligence officer determine the outcome.

  4. Problems with the Tactical Event Resolution Step • Time Constraints • Communication • Biases • Logistics • Simplification by aggregation • Ad hoc combat results

  5. Solution • Automated support for tactical event resolution • Include biases • Track resources • Provide a configurable combat results mechanism

  6. Design • Java-based • Event Resolution components • Genetic Algorithm • Java Expert System Shell (JESS) • an expert system shell and scripting language • supports the development of rule-based expert systems

  7. Architecture (GA Analysis)

  8. User Actions • Create Events • Select Biases • Run analysis • Show results • Reconfigure and rerun as desired

  9. Genetic Analysis Component • Biased Agents perform initial allocations • Maneuver bias • Massed fire support bias • Allocations are made by level and force • Force Summary • Combat Results Mechanism • Fitness monitor assigns a fitness value

  10. Genetic Analysis (cont.) • More-fit allocations have a higher probability of being used to produce the next generation • Configurable probability of crossover • Configurable probability of mutation • Each new generation is evaluated propagated the same way • The most-fit allocation is selected

  11. Genetic Coding

  12. Rule-based Component • Forces allocated • Combat is resolved • Repeated until success or failure • All forces are expended unsuccessfully • Or a force mix is found that is successful • Default bias is to minimize forces used

  13. Rule-based Details • Small number of rules needed (22) • Rules are easy to understand by a human • A point of comparison with GA approach • Can replace combat model as needed

  14. User Interface and Scenario

  15. Conclusions • Easy to setup and fast to run • Allows for “what-if” experimentation • Can playback and show intermediate steps • Gives more choices to the commander

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