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Active Player Modeling in the Iterated Prisoner's Dilemma
The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise m...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775783/ https://www.ncbi.nlm.nih.gov/pubmed/26989405 http://dx.doi.org/10.1155/2016/7420984 |
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author | Park, Hyunsoo Kim, Kyung-Joong |
author_facet | Park, Hyunsoo Kim, Kyung-Joong |
author_sort | Park, Hyunsoo |
collection | PubMed |
description | The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions. |
format | Online Article Text |
id | pubmed-4775783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-47757832016-03-17 Active Player Modeling in the Iterated Prisoner's Dilemma Park, Hyunsoo Kim, Kyung-Joong Comput Intell Neurosci Research Article The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions. Hindawi Publishing Corporation 2016 2016-02-18 /pmc/articles/PMC4775783/ /pubmed/26989405 http://dx.doi.org/10.1155/2016/7420984 Text en Copyright © 2016 H. Park and K.-J. Kim. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Park, Hyunsoo Kim, Kyung-Joong Active Player Modeling in the Iterated Prisoner's Dilemma |
title | Active Player Modeling in the Iterated Prisoner's Dilemma |
title_full | Active Player Modeling in the Iterated Prisoner's Dilemma |
title_fullStr | Active Player Modeling in the Iterated Prisoner's Dilemma |
title_full_unstemmed | Active Player Modeling in the Iterated Prisoner's Dilemma |
title_short | Active Player Modeling in the Iterated Prisoner's Dilemma |
title_sort | active player modeling in the iterated prisoner's dilemma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4775783/ https://www.ncbi.nlm.nih.gov/pubmed/26989405 http://dx.doi.org/10.1155/2016/7420984 |
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