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Design of a real time strategy game with a genetic AI

By Bharat Ponnaluri. Design of a real time strategy game with a genetic AI. Abstract. Current method of AI programming: Functions combinations of evaluation functions+constants Disadvantages of current method Difficult to generate good combinations

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Design of a real time strategy game with a genetic AI

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  1. By Bharat Ponnaluri Design of a real time strategy game with a genetic AI

  2. Abstract • Current method of AI programming: • Functions combinations of evaluation functions+constants • Disadvantages of current method • Difficult to generate good combinations • Difficult when brute force calculations are not feasible • A skilled player can often beat a handicapped AI with a signficant numerical advantage.

  3. Images from: http://www.twcenter.net/forums/showthread.php?t=359530

  4. How do GAs work • Replicate evolution • Steps • Determine random combinations of heuristics and constants(chromosomes) • Test chromosomes • Remove suboptimal chromosomes • Mutate surviving chromosomes • Swap data between surviving chromosomes • If chromosomes are optimal,end algorithm • Otherwise, repeat steps 1-7

  5. Sample chromosome Chromosome     enemyStrength()/2*myStrength()-3*fooA()/43.........+power() Possible genes public int fooA() .... public int enemyStrength()     .... public int myStrength()

  6. Advantages and Disadvantages • Advantages • Can function without user input • Can optimize a large number of constants • Usually is very good at optimizing stuff • Can determine unknown solutions • Possible disadvantages • GAs are complicated • Take a significant amount of computing power • Networking will increase complexity

  7. Heuristic evaluation functions • Heuristic evaluation functions • Used to evaluate the quality of a possible course of action based on game data • Example: • Public int getTrafficJamFactor() • Return ( 1.001^(number of cars))/roadSize

  8. Evolutionary Stable Strategies(ESS) • Gene pool of defectors means cooperating players will lose • Advantages • Diverse AI personalities • Disadvantages • May lead to suboptimal/boring AI's

  9. Important Classes • Classes • FormualaGenerator: • Driver Program • GraphPanel: • Draws the graph of the average fitness and maximum fitness. • Makes the GA run periodically in synchornization with the graph. • Chromosome: • Represents the chromosomes/expressions.

  10. Generating a target number using a mathematical expression • Combination of numbers(1-9) and mathematical operators(-,+,*,/) adding up to a target number • Each number=4 bits • Sample Expressions for 150 • Static Mutation 1.0292804250299605 + ((2.7400839829565777 / 1.0605221382369576) * 7.741718241469524 * 7.316070011134583) - ((-0.9821438516501173) / 4.043144676725913) + (0.9986661909539591 * 6.634650893629718 * 0.3645726822807098) = 150.026535 --Dynamic Mutation 1.0292804250299605 + ((2.7400839829565777 / 1.0605221382369576) * 7.741718241469524 * 7.316070011134583) - ((-0.9821438516501173) / 4.043144676725913) + (0.9986661909539591 * 6.634650893629718 * 0.3645726822807098) = 150.026535

  11. Generating a formula for an input-output pair -Formula: x^4+x^3+x^2+x+1 -Sample Expressions -1*x/3-3*x+8-1*x+9-10*x*7-5*x*9/6*x+10*5*x*6*6*x-3/8*x+7-8*x+4*5*x*4-9*x+4-6 -9*x*9*2*x*9*3*x/3+5*x-5*1*x/9/2*x/7*8*x*1/7*x*5/8*x-4-7*x/5*4*x*4/1*x/4*6 -Conclusion: Polynomials are not recognized.

  12. Analysis of results • Using dynamic muation rates did not help • The GA generally works but occasionally crashes due to a bug which deletes all the chromosomes • The average change and static deviation in fitness does not change • Shows that the GA does not get stuck on local exterma.

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