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The single biggest (and maybe the only) difference between Game AI and Traditional AI is the optimisation function. TAI optimises for correctness, followed by e

The single biggest (and maybe the only) difference between Game AI and Traditional AI is the optimisation function. TAI optimises for correctness, followed by efficiency, and GAI optimises for efficiency, followed by fun. The inapplicability of TAI solutions to GAI problems is because we (the games industry) don't have any kind of theoretical framework for even measuring fun, let alone optimising for it. The state of the art is essentially a handful of ad-hoc models of how people experience games, how they improve and how they have fun (and why they stop having fun). Coupled with that, you have an enormous range of player skill - the lead designer of World of Warcraft estimated that the capability of player groups varied over several orders of magnitude (and he's a former academic professor, so probably speaking literally). Someone in another reply stated that GAI was roughly passing a restricted Turing Test. That seems like a reasonable suggestion to make, but if it's true, then applying TAI frameworks to solve GAI issues is roughly the same difficulty as trying to create a non-Turing-test passing AI that can write a Turing-test-passing AI. It seems intractably difficult with the tools that we have available to us right now.

pmelendez 14y ago
"It seems intractably difficult with the tools that we have available to us right now." I think that's actually the problem rather than the optimization function. There is an vicious circle where lack of tools implies less interest that implies less research than implies lack of tools. I am very reluctant to think that TAI's optimization function would make a difference in terms of techniques usability in GAI problems. I agree that fun factor is not quantifiable under current knowledge. But that doesn't mean that there is not common ground between them. For example, it is not the first time that path finding is being approached using ML: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=953332&contentType=Journals+%26+Magazines http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=tru... http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=117877&contentType=Conference+Publications http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=tru... On the other hand, most games use finite state machines for decision making which could be approached in many different ways: http://ccrg.cs.memphis.edu/assets/papers/NNApaper12.pdf http://ccrg.cs.memphis.edu/assets/papers/NNApaper12.pdf

pmelendez 14y ago

"It seems intractably difficult with the tools that we have available to us right now." I think that's actually the problem rather than the optimization function. There is an vicious circle where lack of tools implies less interest that implies less research than implies lack of tools. I am very reluctant to think that TAI's optimization function would make a difference in terms of techniques usability in GAI problems. I agree that fun factor is not quantifiable under current knowledge. But that doesn't mean that there is not common ground between them. For example, it is not the first time that path finding is being approached using ML: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=953332&contentType=Journals+%26+Magazines http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=tru... http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=117877&contentType=Conference+Publications http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=tru... On the other hand, most games use finite state machines for decision making which could be approached in many different ways: http://ccrg.cs.memphis.edu/assets/papers/NNApaper12.pdf http://ccrg.cs.memphis.edu/assets/papers/NNApaper12.pdf