Abstract
Real-time strategy (RTS) games differ as they persist in varying scenarios and states. These games enable an integrated correspondence of non-player characters (NPCs) to appear as an autodidact in a dynamic environment, thereby resulting in a combined attack of NPCs on human-controlled character (HCC) with maximal damage. This research aims to empower NPCs with intelligent traits. Therefore, we instigate an assortment of ant colony optimization (ACO) with genetic algorithm (GA)-based approach to first-person shooter (FPS) game, i.e., Zombies Redemption (ZR). Eminent NPCs with best-fit genes are elected to spawn NPCs over generations and game levels as yielded by GA. Moreover, NPCs empower ACO to elect an optimal path with diverse incentives and less likelihood of getting shot. The proposed technique ZR is novel as it integrates ACO and GA in FPS games where NPC will use ACO to exploit and optimize its current strategy. GA will be used to share and explore strategy among NPCs. Moreover, it involves an elaboration of the mechanism of evolution through parameter utilization and updation over the generations. ZR is played by 450 players with varying levels having the evolving traits of NPCs and environmental constraints in order to accumulate experimental results. Results revealed improvement in NPCs performance as the game proceeds.
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