Contributors: Feng Shu; Narendra S. Chaudhari

1. Human Expert Players Experience Online Data of NPC N P C Online Behavior Decision SGNN Generation Offline Rule Extraction Rule Base Poster Presentation Code: THU-P1-01 School of Computer Engineering The 3rd Conference on Industrial Electronics and Applications • Behavior Decision Algorithm Using SGNN for Game Characters Background Experiment • Method is applied in computer games to extract the non-player characters’ behavior logic rules based on human knowledge and experience, make the NPCs active reasonable and more like real human beings, and contribute to enhance computer games interest and intelligence ability. • For example, in military games, a NPC soldier’s behavior which maybe include shoot, guard, move and chase are affected by environment parameters and internal attributes. • Game artificial intelligence refers to techniques that make computer and video games to produce the illusion of intelligence in the behavior of non-player characters (NPCs). • The behavior structure of the NPCs is often crucial in the game, since players are already tired about the simple arranged, well-regulated and even stiff actions of the non player creatures. • Game agent is in the need of improvement to meet the increasing demand of game players. Methodology • Our research focus on improving fuzzy logic, neural network, and other related techniques and apply these artificial intelligent computing methods to model the non player characters. • A chaotic behavior decision algorithm is proposed based on self-generating neural network and fuzzy logic. • Offline Behavior Rule Extraction based on SGNN • Online Fuzzy Behavior Decision Algorithm Fig3. Classified behaviors using SGNN Fig1. Self-generated neural network Fig2. Fuzzy function used Fig 4. Flow chart of chaotic behavior decision. Note how the NPCs behavior is decided during progress Contributors: Feng Shu; Narendra S. Chaudhari www.ntu.edu.sg