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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