The current issues of commercial games AI are related to high-level concepts of gaming such as realistic virtual actors, automatic content and storyline generation, dynamic learning, or social behavior. Tackling these issues could represent a qualitative improvement on gaming experience from the player perspective and academic research on AI has good opportunities to provide solutions to these challenges.

We argue that the following issues are a consequence of the use of domain-dependent low-level approaches:

1. •Blind specifications: the NPCs are programmed on how to act in reaction to environmental and/or other players conditions, but not why to act in a given manner; hence, there is no real purpose behind actions taken and, in most cases, these actions do not look “natural” from the human player’s perception.
   

2. •Lack of flexibility/adaptiveness: the rule-based actions are limited and reactive to external conditions, unable to evolve, and providing reduced pro-activeness.
   

3. •Strange behavior: the behavior of the NPCs do no reflect the aspects of sociability and “participating in a whole”, leading to unnatural actions from the human player’s perception.
   

4. •Predictable behavior: NPCs’ tactics are easily discoverable by the human player and, after some time, predictable, leading to negative perception.
   

5. •Low reusability, as the solutions are commonly tailored to specific scenario domains and, therefore, not re-usable through different games even if they belong to the same genre.
   

Our hypothesis is that it is possible to create elaborate solutions for the issues of both individual behavior control and collective strategy techniques by integrating models based on Organization Theoretical methods to control NPCs’ behavior. This theory contributes to the systematic study of how actors behave within organizations. Hence, the actors in a game are described as an organization which behavior is based on specific roles, norms, dependencies, and capabilities.