Modeling Players Personality in General Game Playing
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Typ
Examensarbete för masterexamen
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2018
Författare
Crotti, Stefania
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Artificial agents’ skills need to become more relatable to humans’, and one approach
to solve this problem would be to associate a personality to the agents. When
games are used as a framework, General Game Playing (GGP) provides an unbiased
environment where new games are played without any prior knowledge of the rules,
and without applying any game-dependent heuristic. This thesis is expecting to infer
preferences from human played games, depending on the personality the players
recognised themselves in. The artificial player is aided with a Monte Carlo Tree
Search algorithm with tunable parameters, which associate evaluation values to each
move, consequently selecting the next state. The optimal set of parameters to fit the
human gameplay is found with the subsidy of a Genetic Algorithm where individuals
are represented as sets of parameters themselves. This approach is backed up with
a Bayesian probability model, and, finally, the outputted sets of parameters are
evaluated to determine if the artificial gamer has indeed learnt to behave accordingly
to a certain personality. After an extensive research on personality models has been
carried out to find a suitable one for the amount of data expected to be collected,
the choice has fallen over the Hippocrates’-Galen Four Temperaments. The results
however hint to the conclusion that a different model might have been easier to
be fit. Although the results are not astonishing, this thesis can be considered as a
first stepping stone into personality model fitting through Monte Carlo Tree Search
parameters tuning.
Beskrivning
Ämne/nyckelord
General Game Playing , Monte Carlo Tree Search , Genetic Algorithm , Personality Mapping , Bayesian Modeling