Developing Parallel Dependency Graph In Improving Game Balancing
Sim-Hui Tee

TL;DR
This paper introduces a parallel dependency graph architecture for game assets, enabling dynamic balancing between AI and human players to enhance game fairness and player engagement.
Contribution
It proposes a novel parallel dependency graph architecture that allows adjustable AI dependency graphs for improved game balancing.
Findings
Parallel dependency graphs improve game balancing.
Adjustable AI dependency graphs enhance player engagement.
The approach offers a flexible framework for balancing AI and human assets.
Abstract
The dependency graph is a data architecture that models all the dependencies between the different types of assets in the game. It depicts the dependency-based relationships between the assets of a game. For example, a player must construct an arsenal before he can build weapons. It is vital that the dependency graph of a game is designed logically to ensure a logical sequence of game play. However, a mere logical dependency graph is not sufficient in sustaining the players' enduring interests in a game, which brings the problem of game balancing into picture. The issue of game balancing arises when the players do not feel the chances of winning the game over their AI opponents who are more skillful in the game play. At the current state of research, the architecture of dependency graph is monolithic for the players. The sequence of asset possession is always foreseeable because there…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEducational Games and Gamification · Data Mining Algorithms and Applications
