Non-Player Character (NPC) behaviors in todays computer games are mostly generated from manually written scripts. The high cost of manually creating complex behaviors for each NPC to exhibit intelligence in response to every situation in the game results in NPCs with repetitive and artificial looking behaviors. The goal of this research is to enable NPCs in computer games to exhibit natural and human-like behaviors in non-combat situations. The quality of these behaviors affects the game experience especially in story-based games, which rely heavily on player-NPC interactions. Reinforcement Learning has been used in this research for BioWare Corp.s Neverwinter Nights to learn natural-looking behaviors for companion NPCs. The proposed method enables NPCs to rapidly learn reasonable behaviors and adapt to the changes in the game environment. This research also provides a learning architecture to divide the NPC behavior into sub-behaviors and sub-tasks called decision domains.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1464 |
| Date | 11 1900 |
| Creators | Sharifi, AmirAli |
| Contributors | Szafron, Duane (Computing Science), Szafron, Duane (Computing Science), Bowling, Michael (Computing Science), Gouglas, Sean (History & Classics) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 6885241 bytes, application/pdf |
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