Creating Human-Like Non-Player Game Characters using A Memetic Multi-Agent System

Memetic Multi-Agent System (MeMAS) has recently emerged as a combination of memetic automaton and multi-agent system (MAS), wherein all meme-inspired agents acquire increasing learning capacity and intelligence through meme evolution. This paper further presents a study of MeMAS in developing human-like non-player characters in complex first-person shooter (FPS) games. In particular, we consider a well-known commercial FPS game, known as Unreal Tournament 2004 (UT2004), as our game of interest and discuss the details of non-player characters based on a manifestation of "Temporal Difference-Fusion Architecture for Learning and Cognition" (TDFALCON) neural network. In addition, we present a brief crossdomain study of MeMAS wherein the useful knowledge in the form of memes learned from different yet related simple domains previously solved are used to enhance learning performance of the non-player characters in UT2004. Benchmark experiments are studied to investigate the efficacy of MeMAS in UT2004 from various aspects, including learning efficiency, generalization capability, and computational cost. The empirically results indicate that the MeMAS could clearly improve the learning effectiveness and efficiency of designed non-player characters in UT2004.