DeepAI AI Chat
Log In Sign Up

Modeling Individual and Team Behavior through Spatio-temporal Analysis

by   Sabbir Ahmad, et al.

Modeling players' behaviors in games has gained increased momentum in the past few years. This area of research has wide applications, including modeling learners and understanding player strategies, to mention a few. In this paper, we present a new methodology, called Interactive Behavior Analytics (IBA), comprised of two visualization systems, a labeling mechanism, and abstraction algorithms that use Dynamic Time Warping and clustering algorithms. The methodology is packaged in a seamless interface to facilitate knowledge discovery from game data. We demonstrate the use of this methodology with data from two multiplayer team-based games: BoomTown, a game developed by Gallup, and DotA 2. The results of this work show the effectiveness of this method in modeling, and developing human-interpretable models of team and individual behavior.


page 3

page 4

page 7

page 8

page 9


Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2

Multiplayer Online Battle Arena (MOBA) games are among the most played d...

Presenting Multiagent Challenges in Team Sports Analytics

This paper draws correlations between several challenges and opportuniti...

Battle Ground: Data Collection and Labeling of CTF Games to Understand Human Cyber Operators

Industry standard frameworks are now widespread for labeling the high-le...

Spatio-Temporal Analysis of Team Sports -- A Survey

Team-based invasion sports such as football, basketball and hockey are s...

The Quest for Omnioculars: Embedded Visualization for Augmenting Basketball Game Viewing Experiences

Sports game data is becoming increasingly complex, often consisting of m...

"And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis

Many successful games rely heavily on data analytics to understand playe...