Repository logo
 

StABLE: Making Player Modeling Possible for Sandbox Games

Date

2020-02-18

Journal Title

Journal ISSN

Volume Title

Publisher

ORCID

0000-0002-9977-2662

Type

Thesis

Degree Level

Masters

Abstract

Digital games are increasingly delivered as services. Understanding how players interact with games on an ongoing basis is important for maintenance. Logs of player activity offer a potentially rich window into how and why players interact with games, but can be difficult to render into actionable insights because of their size and complexity. In particular, understanding the sequential behavior in-game logs can be difficult. In this thesis, we present the String Analysis of Behavior Log Elements (StABLE) method, which renders location and activity data from a game log file into a sequence of symbols which can be analyzed using techniques from text mining. We show that by intelligently designing sequences of features, it is possible to cluster players into groups corresponding to experience or motivation by analyzing a dataset containing Minecraft game logs. The findings demonstrate the validity of the proposed method, and illustrate its potential utility in mining readily available data to better understand player behavior.

Description

Keywords

Log analysis, Bag of Words, movement, motivation, experience, data mining, data analytics

Citation

Degree

Master of Science (M.Sc.)

Department

Computer Science

Program

Computer Science

Part Of

item.page.relation.ispartofseries

DOI

item.page.identifier.pmid

item.page.identifier.pmcid