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Thomas Gaertner

Tuesday 3rd September 2013

Time: 4pm

 

Basement Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

An Online Learning Algorithm for
Dynamic Difficulty Adjustment in Computer Games

 

While difficulty adjustment is common practise in many traditional games (consider, for instance, the handicaps in Golf and Go), the case for dynamic difficulty adjustment in electronic games has been made only recently. To date, most computer games only have static difficulty settings and computer game researchers have proposed a number of heuristic approaches. In this talk, I (i) formalise dynamic difficulty adjustment as a learning problem on partially ordered sets, (ii) propose an exponential update algorithm for this setting, (iii) show a bound on the number of wrong difficulty settings relative to the best static setting chosen in hindsight, and (iv) demonstrate the empirical performance of the algorithm.