Massively Multiplayer Online Role Playing Games (MMORPGs) are a prominent genre in today's video game industry with the most popular MMORPGs generating billions of dollars in revenue and attracting millions of players. As they have grown, they have become a major target for both technological research and sociological research. In such research, it is nearly impossible to reach the same player scale from any self-made technology or sociological experiments. This greatly limits the amount of control and topics that can be explored. In an effort to make up a lacking or non-existent player-base for custom-made MMORPG research scenarios A.I. agents, impersonating human players, can be used to "bootstrap" the research scenario to reach the necessary massive number of players that define the game genre. This thesis presents a system that makes its human players and A.I. players indistinguishable while preserving the basic characteristics of a typical MMORPG. To better achieve identical perception of human and A.I. players, our system centers around the collection, sharing, and exchange of information while limiting the means of expression and actions of players. A gameplay scenario built on the Panoptyk engine was constructed to imitate gameplay experienced in major MMORPGs. We conducted a user-study where subjects play through the scenario with a varying number of A.I. players unknown to them. Three versions of the scenario were created to assess how indistinguishable human and A.I. players were and vice versa. We found, across 24 participants, there were 32% correct identifications, 30% incorrect identifications, and 38% answers of "I don't know". This was broken down into 20% correct identifications, 42% incorrect identifications, and 38% answers of "I don't know" for bot characters and 46% correct identifications, 16% incorrect identifications, and 38% answers of ``I don't know'' for human characters.

Massively multiplayer online role-playing games (MMORPGs) are one of the most popular genres in video games that combine massively multiplayer online genres with role-playing gameplay. MMORPGs’ featured social interaction and forms of level pro- gression through quest completion are the core for gaining players’ attention. Varied and challenging quests play an essential part in retaining that attention. However, well-crafted content takes much longer to develop with human efforts than it does to consume, and the dominant procedural content generation models for quests suffer from the drawback of being incompatible with dynamic world changes and the feeling of repetition over time. The proposed solution is to dynamically generate contextu- ally relevant quests with real-time information collected from the players. This thesis is to show that it is possible to generate contextually dynamic quests that could fit into the background story with only the information gathered from the players in real-time. To validate the proposed solution, a text-based MMO scenario is generated from the Panoptyk Engine, an information-driven game engine designed to run massively multiplayer online games (MMOs) without the distinction between NPCs and human players. The engine is intended to serve as a platform for simulating human/robot interaction. In this case, a quest simulation is built where quests are given by the NPC faction leaders utilizing the information gathered through ongoing events; players or robots belonging to certain factions will be taking and completing the quests. ivThe results of the 14-person survey showed some successes and some limitations. There was strong agreement in players understanding of what the quest was asking for, quests were related to other agents the player had seen in the world, quests involved agents that the player had interactions with, quests were related to things happening in the game world, quests were interesting. There was regular agreement on players understanding of quests that were designed entirely before the player started playing, quests were generated using a basic template with details filled in based on the player’s actions, and quests were dynamically generated. It was inconclusive with players understanding of quest motivation and previous quests the player had completed having an impact on the next quests.