Online learning and mining human play in complex games

Mihai Dobre, Alex Lascarides

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We propose a hybrid model for automatically acquiring a policy for a complex game, which combines online learning with mining knowledge from a corpus of human game play. Our hypothesis is that a player that learns its policies by combining (online) exploration with biases towards human behaviour that’s attested in a corpus of humans playing the game will outperform any agent that uses only one of the knowledge sources. During game play, the agent extracts similar moves made by players in the corpus in similar situations, and approximates their utility alongside other possible options by performing simulations from its current state. We implement and assess our model in an agent playing the complex win-lose board game Settlers of Catan, which lacks an implementation that would challenge a human expert. The results from the preliminary set of experiments illustrate the potential of such a joint model.
Original languageEnglish
Title of host publicationComputational Intelligence and Games (CIG), 2015 IEEE Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)978-1-4799-8621-7
Publication statusPublished - 2015


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