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\n  \n 2017\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Time series and case-based reasoning for an intelligent tetris game.\n \n \n \n\n\n \n Ariza, D.; Sánchez-Ruiz, A.; and González-Calero, P.\n\n\n \n\n\n\n Volume 10339 LNAI 2017.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@book{\n title = {Time series and case-based reasoning for an intelligent tetris game},\n type = {book},\n year = {2017},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {Case-based reasoning,Dynamic difficulty adjustment,K-nearest neighbor,Tetris,Time series,Video games},\n volume = {10339 LNAI},\n id = {4b0dafbd-e4e9-320f-97b3-b5a43c415a78},\n created = {2018-12-19T15:42:43.347Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {286bc995-c08d-3116-845d-e05f9706a57f},\n last_modified = {2018-12-19T15:42:43.347Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© Springer International Publishing AG 2017. One of the biggest challenges when designing videogames is to keep a player’s engagement. Designers try to adapt the game experience for each player defining different difficulty levels or even different sets of behaviors that the non-player characters will use depending on the player profile. It is possible to use different machine learning techniques to automatically classify players in broader groups with distinctive behaviors and then dynamically adjust the game for those types of players. In this paper, we present a case-based approach to detect the skill level of the players in the Tetris game. Cases are extracted from previous game traces and contain time series describing the evolution of a few parameters during the game. Once we know the player level, we adapt the difficulty of the game dynamically providing better or worse Tetris pieces. Our experiments seem to confirm that this type of dynamic difficulty adjustment improves the satisfaction of the players.},\n bibtype = {book},\n author = {Ariza, D.S.L. and Sánchez-Ruiz, A.A. and González-Calero, P.A.},\n doi = {10.1007/978-3-319-61030-6_13}\n}
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\n\n\n
\n © Springer International Publishing AG 2017. One of the biggest challenges when designing videogames is to keep a player’s engagement. Designers try to adapt the game experience for each player defining different difficulty levels or even different sets of behaviors that the non-player characters will use depending on the player profile. It is possible to use different machine learning techniques to automatically classify players in broader groups with distinctive behaviors and then dynamically adjust the game for those types of players. In this paper, we present a case-based approach to detect the skill level of the players in the Tetris game. Cases are extracted from previous game traces and contain time series describing the evolution of a few parameters during the game. Once we know the player level, we adapt the difficulty of the game dynamically providing better or worse Tetris pieces. Our experiments seem to confirm that this type of dynamic difficulty adjustment improves the satisfaction of the players.\n
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\n  \n 2016\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Dynamic Difficulty Adjustment in Tetris.\n \n \n \n \n\n\n \n Lora, D.; Sánchez-Ruiz, A., A.; González-Calero, P., A.; and Gómez-Mart\\'\\in, M., A.\n\n\n \n\n\n\n In Markov, Z.; and Russell, I., editor(s), Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016, Key Largo, Florida, May 16-18, 2016., pages 335-339, 2016. AAAI Press\n \n\n\n\n
\n\n\n\n \n \n \"DynamicWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 19 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Dynamic Difficulty Adjustment in Tetris},\n type = {inproceedings},\n year = {2016},\n pages = {335-339},\n websites = {http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/view/12824},\n publisher = {AAAI Press},\n id = {a5563764-ee6a-3a84-8b87-a81d35c06f83},\n created = {2018-12-19T15:42:43.230Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {286bc995-c08d-3116-845d-e05f9706a57f},\n last_modified = {2018-12-19T15:42:43.230Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {DBLP:conf/flairs/LoraSGG16},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Lora, Diana and Sánchez-Ruiz, Antonio A and González-Calero, Pedro A and Gómez-Mart\\'\\in, Marco Antonio},\n editor = {Markov, Zdravko and Russell, Ingrid},\n booktitle = {Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016, Key Largo, Florida, May 16-18, 2016.}\n}
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\n \n\n \n \n \n \n \n Difficulty adjustment in tetris with time series.\n \n \n \n\n\n \n Lora, D.; Sánchez-Ruiz, A.; and González-Calero, P.\n\n\n \n\n\n\n In CEUR Workshop Proceedings, volume 1682, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Difficulty adjustment in tetris with time series},\n type = {inproceedings},\n year = {2016},\n keywords = {Casebased reasoning,Dynamic difficulty adjustment,Tetris,Time series},\n volume = {1682},\n id = {44bf65fa-6b04-324a-a535-61e2a6f510aa},\n created = {2018-12-19T15:42:43.339Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {286bc995-c08d-3116-845d-e05f9706a57f},\n last_modified = {2018-12-19T15:42:43.339Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Keeping a player within the flow in a game is a central goal for game designers, making the game neither too easy nor too hard. Dynamic Difficulty adjustment seeks to fulfill this goal by dynamically tuning difficulty according to player actions in the game. In this paper we demonstrate that case-based reasoning with time series can serve to automatically identify the ability of a player in a game, and thus serve as the input for difficulty adjustment based on player ability. We try different configurations for the generation of the case base from game logs, and compare them in terms of how well they classify player ability.},\n bibtype = {inproceedings},\n author = {Lora, D. and Sánchez-Ruiz, A.A. and González-Calero, P.A.},\n booktitle = {CEUR Workshop Proceedings}\n}
\n
\n\n\n
\n Keeping a player within the flow in a game is a central goal for game designers, making the game neither too easy nor too hard. Dynamic Difficulty adjustment seeks to fulfill this goal by dynamically tuning difficulty according to player actions in the game. In this paper we demonstrate that case-based reasoning with time series can serve to automatically identify the ability of a player in a game, and thus serve as the input for difficulty adjustment based on player ability. We try different configurations for the generation of the case base from game logs, and compare them in terms of how well they classify player ability.\n
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\n  \n 2011\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Predicting performance in team games: The automatic coach.\n \n \n \n\n\n \n Jiménez-Díaz, G.; Menéndez, H.; Camacho, D.; and González-Calero, P.\n\n\n \n\n\n\n In ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, volume 1, pages 401-406, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Predicting performance in team games: The automatic coach},\n type = {inproceedings},\n year = {2011},\n keywords = {Clustering,Videogames},\n pages = {401-406},\n volume = {1},\n id = {2165a535-6543-37a8-a036-c4a968cafb41},\n created = {2018-12-19T15:42:43.238Z},\n file_attached = {false},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {286bc995-c08d-3116-845d-e05f9706a57f},\n last_modified = {2018-12-19T15:42:43.238Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {A wide range of modern videogames involves a number of players collaborating to obtain a common goal. The way the players are teamed up is usually based on a measure of performance that makes players with a similar level of performance play together. We propose a novel technique based on clustering over observed behaviour in the game that seeks to exploit the particular way of playing of every player to find other players with a gameplay such that in combination will constitute a good team, in a similar way to a human coach. This paper describes the preliminary results using these techniques for the characterization of player and team behaviours. Experiments are performed in the domain of Soccerbots.},\n bibtype = {inproceedings},\n author = {Jiménez-Díaz, G. and Menéndez, H.D. and Camacho, D. and González-Calero, P.A.},\n booktitle = {ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence}\n}
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\n A wide range of modern videogames involves a number of players collaborating to obtain a common goal. The way the players are teamed up is usually based on a measure of performance that makes players with a similar level of performance play together. We propose a novel technique based on clustering over observed behaviour in the game that seeks to exploit the particular way of playing of every player to find other players with a gameplay such that in combination will constitute a good team, in a similar way to a human coach. This paper describes the preliminary results using these techniques for the characterization of player and team behaviours. Experiments are performed in the domain of Soccerbots.\n
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\n  \n 2007\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Adjusting game difficulty level through Formal Concept Analysis.\n \n \n \n \n\n\n \n Gómez-Martín, M.; Gómez-Martín, P.; Gonzâlez-Calero, P.; and Díaz-Agudo, B.\n\n\n \n\n\n\n In Research and Development in Intelligent Systems XXIII - Proceedings of AI 2006, the 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pages 217-230, 2007. \n \n\n\n\n
\n\n\n\n \n \n \"AdjustingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 21 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Adjusting game difficulty level through Formal Concept Analysis},\n type = {inproceedings},\n year = {2007},\n pages = {217-230},\n id = {8d9f2c40-68eb-30bd-8b7b-c02c7d2a7df2},\n created = {2018-12-19T15:53:20.232Z},\n file_attached = {true},\n profile_id = {7ff3d559-34c5-3dc7-a15e-4809d39e6685},\n group_id = {286bc995-c08d-3116-845d-e05f9706a57f},\n last_modified = {2018-12-19T16:10:12.510Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In order to reach as many players as possible, videogames usually allow the user to choose the difficulty level. To do it, game designers have to decide the values that some game parameters will have depending on that decision. In simple videogames this is almost trivial: minesweeper is harder with longer board sizes and number of mines. In more complex games, game designers may take advantage of data mining to establish which of all the possible parameters will affect positively to the player experience. This paper describes the use of Formal Concept Analysis to help to balance the game using the logs obtained in the tests made prior the release of the game. © 2007 Springer-Verlag London.},\n bibtype = {inproceedings},\n author = {Gómez-Martín, M.A. and Gómez-Martín, P.P. and Gonzâlez-Calero, P.A. and Díaz-Agudo, B.},\n doi = {10.1007/978-1-84628-663-6-16},\n booktitle = {Research and Development in Intelligent Systems XXIII - Proceedings of AI 2006, the 26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence}\n}
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\n In order to reach as many players as possible, videogames usually allow the user to choose the difficulty level. To do it, game designers have to decide the values that some game parameters will have depending on that decision. In simple videogames this is almost trivial: minesweeper is harder with longer board sizes and number of mines. In more complex games, game designers may take advantage of data mining to establish which of all the possible parameters will affect positively to the player experience. This paper describes the use of Formal Concept Analysis to help to balance the game using the logs obtained in the tests made prior the release of the game. © 2007 Springer-Verlag London.\n
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