TY - GEN
T1 - Hyper-heuristic general video game playing
AU - Mendes, Andre
AU - Togelius, Julian
AU - Nealen, Andy
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - In general video game playing, the challenge is to create agents that play unseen games proficiently. Stochastic tree search algorithms, like Monte Carlo Tree Search, perform relatively well on this task. However, performance is non-transitive: different agents perform best in different games, which means that there is not a single agent that is the best in all the games. Rather, some types of games are dominated by a few agents whereas other different agents dominate other types of games. Thus, it should be possible to construct a hyper-agent that selects from a portfolio, in which constituent sub-agents will play a new game best. Since there is no knowledge about the games, the agent needs to use available features to predict the most suitable algorithm. This work constructs such a hyper-agent using the General Video Game Playing Framework (GVGAI). The proposed method achieves promising results that show the applicability of hyper-heuristics in general video game playing and related tasks.
AB - In general video game playing, the challenge is to create agents that play unseen games proficiently. Stochastic tree search algorithms, like Monte Carlo Tree Search, perform relatively well on this task. However, performance is non-transitive: different agents perform best in different games, which means that there is not a single agent that is the best in all the games. Rather, some types of games are dominated by a few agents whereas other different agents dominate other types of games. Thus, it should be possible to construct a hyper-agent that selects from a portfolio, in which constituent sub-agents will play a new game best. Since there is no knowledge about the games, the agent needs to use available features to predict the most suitable algorithm. This work constructs such a hyper-agent using the General Video Game Playing Framework (GVGAI). The proposed method achieves promising results that show the applicability of hyper-heuristics in general video game playing and related tasks.
UR - http://www.scopus.com/inward/record.url?scp=85015393291&partnerID=8YFLogxK
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U2 - 10.1109/CIG.2016.7860398
DO - 10.1109/CIG.2016.7860398
M3 - Conference contribution
AN - SCOPUS:85015393291
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
PB - IEEE Computer Society
T2 - 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
Y2 - 20 September 2016 through 23 September 2016
ER -