Detecting long-range cause-effect relationships in game provenance graphs with graph-based representation learning-Reference-Cited by-同舟云学术

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Detecting long-range cause-effect relationships in game provenance graphs with graph-based representation learning

Published:2019-12 Issue: Volume:32 Page:100318
ISSN:1875-9521
Container-title:Entertainment Computing
language:en
Short-container-title:Entertainment Computing

Author:

Melo Sidney Araujo,Paes Aline,Clua Esteban Walter Gonzalez,Kohwalter Troy Costa,Murta Leonardo Gresta Paulino

Funder

CAPES

CNPq

FAPERJ

Publisher

Elsevier BV

Subject

Human-Computer Interaction,Software

Reference44 articles.

1. Extending reinforcement learning to provide dynamic game balancing;Andrade,2005

2. Demonstrating the feasibility of automatic game balancing;Volz,2016

3. Predicting player behavior in tomb raider: Underworld;Mahlmann,2010

4. Defining personas in games using metrics;Tychsen,2008

5. Monte-carlo tree search for simulation-based strategy analysis;Zook,2015

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using provenance and replay for qualitative analysis of gameplay sessions;Entertainment Computing;2025-01

2. Game provenance graph-based representation learning vs metrics-based machine learning: An empirical comparison on predictive game analytics tasks;Entertainment Computing;2025-01

3. Using Provenance and Replay for Qualitative Analysis of Gameplay Sessions;2024

4. Encoding feature set information in heterogeneous graph neural networks for game provenance;Applied Intelligence;2023-10-21

5. Prov-Replay: A Qualitative Analysis Framework for Gameplay Sessions Using Provenance and Replay;Entertainment Computing – ICEC 2023;2023

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