Paper Info
Title
A binary classification approach for automatic preference modeling of virtual agents in Civilization IV
Abstract
Player Modeling tries to model players behaviors and characteristics during a game. When these are related to more abstract preferences, the process is normally called Preference Modeling. In this paper we infer Civilization IV's virtual agents preferences with classifiers based on support vector machines. Our vectors contain score indicators from agents gameplay, allowing us to predict preferences based on the indirect observations of actions. We model this task as a binary classification problem, allowing us to make more precise inference. In this sense, we leveraged previous approaches that also used kernel machines but relied on different preference levels. Using binary classification and parameter optimization, our method is able to predict some agents preferences with an accuracy of 100%. Moreover, it is also capable of generalizing to different agents, being able to predict preferences of agents that were not used in the training process.
Year
DOI
Venue
2012
10.1109/CIG.2012.6374151
Computational Intelligence and Games
Keywords
Field
DocType
behavioural sciences,computer games,inference mechanisms,pattern classification,software agents,support vector machines,agent gameplay,automatic Civilization IV virtual agent preference modeling,binary classification approach,inference,kernel machines,parameter optimization,player behavior modelling,player characteristics modelling,player modeling,score indicators,support vector machines,training process
Kernel (linear algebra),Binary classification,Computer science,Inference,Simulation,Generalization,Support vector machine,Software agent,Supervised learning,Unsupervised learning,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-1192-2
1
0.37
References 
Authors
11
3
Name
Order
Citations
PageRank
Marlos C. Machado113514.48
Gisele L. Pappa231.45
Luiz Chaimowicz346747.24