Details
| Original language | English |
|---|---|
| Pages (from-to) | 249-262 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Games |
| Volume | 13 |
| Issue number | 3 |
| Early online date | 8 Jul 2020 |
| Publication status | Published - Sept 2021 |
| Externally published | Yes |
Abstract
This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.
Keywords
- Belief revision, Forward Model Approximation (FMA), general video games, Hierarchical Knowledge Bases (HKBs), Monte Carlo Tree Search (MCTS)
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IEEE Transactions on Games, Vol. 13, No. 3, 09.2021, p. 249-262.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Exception-Tolerant Hierarchical Knowledge Bases for Forward Model Learning
AU - Apeldoorn, Daan
AU - Dockhorn, Alexander
N1 - Funding Information: Manuscript received January 20, 2020; revised April 25, 2020; accepted June 19, 2020. Date of publication July 8, 2020; date of current version September 15, 2021. This work was supported by IMBEI. (Corresponding author: Alexander Dockhorn.) Daan Apeldoorn is with the Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI) in the Medical Informatics Department, University Medical Centre, Johannes Gutenberg University Mainz, 55122 Mainz, Germany (e-mail: daan.apeldoorn@uni-mainz.de).
PY - 2021/9
Y1 - 2021/9
N2 - This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.
AB - This article provides an overview of the recently proposed forward model approximation framework for learning games of the general video game artificial intelligence (GVGAI) framework. In contrast to other general game-playing algorithms, the proposed agent model does not need a full description of the game but can learn the game's rules by observing game state transitions. Based on hierarchical knowledge bases, the forward model can be learned and revised during game-play, improving the accuracy of the agent's state predictions over time. This allows the application of simulation-based search algorithms and belief revision techniques to previously unknown settings. We show that the proposed framework is able to quickly learn a model for dynamic environments in the context of the GVGAI framework.
KW - Belief revision
KW - Forward Model Approximation (FMA)
KW - general video games
KW - Hierarchical Knowledge Bases (HKBs)
KW - Monte Carlo Tree Search (MCTS)
UR - http://www.scopus.com/inward/record.url?scp=85115247260&partnerID=8YFLogxK
U2 - 10.1109/TG.2020.3008002
DO - 10.1109/TG.2020.3008002
M3 - Article
AN - SCOPUS:85115247260
VL - 13
SP - 249
EP - 262
JO - IEEE Transactions on Games
JF - IEEE Transactions on Games
SN - 2475-1502
IS - 3
ER -