seeking rogue) and challenge can probably border on submission. In addition to an involved plot full of intrigue and suspense, the player will expect coordinated activity on the part of opponents – but probably a lot less emotional expression. If anything, agents should express fear and loathing at the very hint of his presence.
Dynamics might include the ability to earn or purchase powerful weapons and spy equipment, and to develop tactics and techniques for stealthy movement, deceptive behavior, evasion and escape. Mechanics include expansive tech and skill trees, a variety of enemy unit types, and levels or areas with variable ranges of mobility, visibility and field of view and so on.
Agents in this space, in addition to coordinating movement and attacks must operate over a wide range of sensory data. Reasoning about the player’ s position and intent should indicate challenge, but promote their overall success. Will enemies be able to pass over obstacles and navigate challenging terrain, or will you“ cheat”? Will sound propagation be“ realistic” or will simple metrics based on distance suffice?
Wrapping Up
Here we see that simple changes in the aesthetic requirements of a game will introduce mechanical changes for its AI on many levels – sometimes requiring the development of entirely new systems for navigation, reasoning, and strategic problem solving.
Conversely, we see that there are no“ AI mechanics” as such – intelligence or coherence comes from the interaction of AI logic with gameplay logic. Using the MDA framework, we can reason explicitly about aesthetic goals, draw out dynamics that support those goals, and then scope the range of our mechanics accordingly. better decompose that experience, and use it to fuel new designs, research and criticism respectively.
References
Barwood, H. & Falstein, N. 2002.“ More of the 400: Discovering Design Rules”. Lecture at Game Developers Conference, 2002. Available online at: http:// www. gdconf. com / archives / 2002 / hal _ barwood. ppt
Church, D. 1999.“ Formal Abstract Design Tools.” Game Developer, August 1999. San Francisco, CA: CMP Media. Available online at: http:// www. gamasutra. com / features / 19990716 / design _ tool s _ 01. htm
Hunicke, R. 2004.“ AI Babysitter Elective”. Lecture at Game Developers Conference Game Tuning Workshop, 2004. In LeBlanc et al., 2004a. Available online at: http:// algorithmancy. 8kindsoffun. com / GDC2004 / AITutori al5. ppt
LeBlanc, M., ed. 2004a.“ Game Design and Tuning Workshop Materials”, Game Developers Conference 2004. Available online at: http:// algorithmancy. 8kindsoffun. com / GDC2004 /
LeBlanc, M. 2004b.“ Mechanics, Dynamics, Aesthetics: A Formal Approach to Game Design.” Lecture at Northwestern University, April 2004. Available online at: http:// algorithmancy. 8kindsoffun. com / MDAnwu. ppt
Conclusions
MDA supports a formal, iterative approach to design and tuning. It allows us to reason explicitly about particular design goals, and to anticipate how changes will impact each aspect of the framework and the resulting designs / implementations.
By moving between MDA’ s three levels of abstraction, we can conceptualize the dynamic behavior of game systems. Understanding games as dynamic systems helps us develop techniques for iterative design and improvement – allowing us to control for undesired outcomes, and tune for desired behavior.
In addition, by understanding how formal decisions about gameplay impact the end user experience, we are able to
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