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DC Field | Value | Language |
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dc.contributor.author | Khalifa, Ahmed | - |
dc.contributor.author | Isaksen, Aaron | - |
dc.contributor.author | Togelius, Julian | - |
dc.contributor.author | Nealen, Andy | - |
dc.date.accessioned | 2021-10-12T05:31:42Z | - |
dc.date.available | 2021-10-12T05:31:42Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Khalifa, A., Isaksen, A., Togelius, J., & Nealen, A. (2016). Modifying MCTS for human-like general video game playing. IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial, New York. 2514-2520. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/81986 | - |
dc.description.abstract | We address the problem of making general video game playing agents play in a human-like manner. To this end, we introduce several modifications of the UCT formula used in Monte Carlo Tree Search that biases action selection towards repeating the current action, making pauses, and limiting rapid switching between actions. Playtraces of human players are used to model their propensity for repeated actions; this model is used for biasing the UCT formula. Experiments show that our modified MCTS agent, called BoT, plays quantitatively similar to human players as measured by the distribution of repeated actions. A survey of human observers reveals that the agent exhibits human-like playing style in some games but not others. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Association for the Advancement of Artificial Intelligence | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Computer games | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Modifying MCTS for human-like general video game playing | en_GB |
dc.type | conferenceObject | 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.bibliographicCitation.conferencename | 25th International Joint Conference on Artificial Intelligence IJCAI-16 | en_GB |
dc.bibliographicCitation.conferenceplace | New York, United States, 09-15/07/2017 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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File | Description | Size | Format | |
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Modifying_MCTS_for_human-like_general_video_game_playing_2016.pdf | 209.86 kB | Adobe PDF | View/Open |
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