In this paper we propose an architecture for specifying the interaction of non-player characters (NPCs) in the game-world in a way that abstracts common tasks in four main conceptual components, namely perception, deliberation, control, action. We argue that this architecture, inspired by AI research on autonomous agents and robots, can offer a number of benefits in the form of abstraction, modularity, re-usability and higher degrees of personalization for the behavior of each NPC. We also show how this architecture can be used to tackle a simple scenario related to the navigation of NPCs under incomplete information about the obstacles that may obstruct the various way-points in the game, in a simple and effective way.
There is a variety of work that aims for action-based AI for NPCs. Traditionally, a combination of scripts and finite state machine are used in interactive games for controlling NPCs. These methods, even if fairly limited, allow the game designer to control every aspect of the NPCs actions. This approach has been employed in different types of succesfull videogames such as the Role Playing Game (RPG) or the First Person Shooter (FPS) . Scripts are written off-line in a high-level language and are used to define simple behaviors for NPCs. Procedural script generation has been proposed in @cite_5 by using simple patter templates which are tuned and combined by hand. Complex behaviors can be developed in a short amount of time but many of the intricacies of the classical scripting approach are not solved and it remains difficult to manage the NPCs as the complexity of the virtual world increases.
@cite_5
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Recently, some researchers have argued that generative design patterns (GDPs) can leverage the obvious design re-use that characterizes traditional design patterns into code re-use. This work provides additional evidence that GDPs are both useful and productive. Specifically, the current state-of-the-art in the domain of computer games is to script individual game objects to provide the desired interactions for each game adventure. We use BioWare Corp.'s popular Neverwinter Nights computer role-playing game to show how GDPs can be used to generate game scripts. This is a particularly good domain for GDPs, since game designers often have little or no programming skills. We demonstrate our approach using a new GDP tool called ScriptEase.