Multi objective optimization based intelligent agent for NPC behavior decision

The main actor of the game is based on non-playable character (NPC) behavior to respond the environment based on artificial intelligent method. This research simulates the behavior of buyer-seller agent on purchasing computer goods in computer game. The buyer agent has price and specification variable which is reacted in satisfaction factor of agent. The seller agent has price and profit variable which is took effect in Join Utility (JU) of agent. In this case, there is usually no single optimal solution, but a set of alternatives with different trade-offs. This research describes buyer-seller agent behavior by multi objective optimizations approach using Multi Objective Evolutionary Optimization (MOEA) Non Sorted Dominated Genetic Algorithm II (NSGA II). NSGA II provides pareto fronts value to the minimum and maximum functions. Based on simulation result., we generate 3 kinds of scenarios of buyer and seller agent. First, the seller agent with profit oriented behavior provides the value of JU twice from the buyers function. Second, the seller agent with customer oriented behavior provides balance JU from the buyer function. Third, the buyer agent with satisfaction oriented behavior. Stability results of simulation is evenly attained after the fifth generation with simulation parameters: chromosome/pop=1000, crossover probability (pc)=0.9, mutation probability (pm)=0.005, index of distribution crossover (ηc)=20., index of distribution mutation (ηm) =20, value of pool=pop/2 and number of tour=2.