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DC Field | Value | Language |
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dc.contributor.author | Sfikas, Konstantinos | - |
dc.contributor.author | Liapis, Antonios | - |
dc.date.accessioned | 2021-09-07T05:58:43Z | - |
dc.date.available | 2021-09-07T05:58:43Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Sfikas, K., & Liapis, A. (2021). Playing against the board : rolling horizon evolutionary algorithms against pandemic. IEEE Transactions on Games. DOI:10.1109/TG.2021.3069766 | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/80756 | - |
dc.description.abstract | Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IEEE | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Board games | en_GB |
dc.subject | Artificial intelligence -- Educational applications | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Application software | en_GB |
dc.title | Playing against the board : rolling horizon evolutionary algorithms against pandemic | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/TG.2021.3069766 | - |
dc.publication.title | IEEE Transactions on Games | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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Playing_Against_the_Board_Rolling_Horizon_Evolutionary_Algorithms_Against_Pandemic.pdf Restricted Access | 871.78 kB | Adobe PDF | View/Open Request a copy |
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