Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis

• URL: http://arxiv.org/abs/2408.17180v1
• Date: Fri, 30 Aug 2024 10:28:36 GMT
• Title: Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
• Authors: Chiu-Chou Lin, Yu-Wei Shih, Kuei-Ting Kuo, Yu-Cheng Chen, Chien-Hua Chen, Wei-Chen Chiu, I-Chen Wu,
• Abstract summary: We develop measures to quantify balance in zero-sum competitive scenarios. We identify useful categories of compositions and pinpoint their counter relationships. Our framework has been validated in popular online games, including Age of Empires II, Hearthstone, Brawl Stars, and League of Legends.
• Score: 24.683917771144536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such as hero combinations in multiplayer online battle arena (MOBA) games or decks in card games-is essential for enhancing gameplay and achieving balance. We have developed two advanced measures that extend beyond the simplistic win rate to quantify balance in zero-sum competitive scenarios. These measures are derived from win value estimations, which employ strength rating approximations via the Bradley-Terry model and counter relationship approximations via vector quantization, significantly reducing the computational complexity associated with traditional win value estimations. Throughout the learning process of these models, we identify useful categories of compositions and pinpoint their counter relationships, aligning with the experiences of human players without requiring specific game knowledge. Our methodology hinges on a simple technique to enhance codebook utilization in discrete representation with a deterministic vector quantization process for an extremely small state space. Our framework has been validated in popular online games, including Age of Empires II, Hearthstone, Brawl Stars, and League of Legends. The accuracy of the observed strength relations in these games is comparable to traditional pairwise win value predictions, while also offering a more manageable complexity for analysis. Ultimately, our findings contribute to a deeper understanding of PvP game dynamics and present a methodology that significantly improves game balance evaluation and design.

Related papers

• Imperfect-Recall Games: Equilibrium Concepts and Their Complexity [74.01381499760288]
  We investigate optimal decision making under imperfect recall, that is, when an agent forgets information it once held before. In the framework of extensive-form games with imperfect recall, we analyze the computational complexities of finding equilibria in multiplayer settings.
  arXiv  Detail & Related papers  (2024-06-23T00:27:28Z)
• Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games [21.168085154982712]
  equilibria in multiplayer games are neither unique nor non-exploitable. This paper takes an initial step towards addressing these challenges by focusing on the natural objective of equal share. We design a series of efficient algorithms, inspired by no-regret learning, that provably attain approximate equal share across various settings.
  arXiv  Detail & Related papers  (2024-06-06T15:59:17Z)
• Neural Population Learning beyond Symmetric Zero-sum Games [52.20454809055356]
  We introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated (CCE) of the game. Our work shows that equilibrium convergent population learning can be implemented at scale and in generality.
  arXiv  Detail & Related papers  (2024-01-10T12:56:24Z)
• Estimation of control area in badminton doubles with pose information from top and back view drone videos [11.679451300997016]
  We present the first annotated drone dataset from top and back views in badminton doubles. We propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance.
  arXiv  Detail & Related papers  (2023-05-07T11:18:39Z)
• Learning Correlated Equilibria in Mean-Field Games [62.14589406821103]
  We develop the concepts of Mean-Field correlated and coarse-correlated equilibria. We show that they can be efficiently learnt in emphall games, without requiring any additional assumption on the structure of the game.
  arXiv  Detail & Related papers  (2022-08-22T08:31:46Z)
• Collusion Detection in Team-Based Multiplayer Games [57.153233321515984]
  We propose a system that detects colluding behaviors in team-based multiplayer games. The proposed method analyzes the players' social relationships paired with their in-game behavioral patterns. We then automate the detection using Isolation Forest, an unsupervised learning technique specialized in highlighting outliers.
  arXiv  Detail & Related papers  (2022-03-10T02:37:39Z)
• Towards convergence to Nash equilibria in two-team zero-sum games [17.4461045395989]
  Two-team zero-sum games are defined as multi-player games where players are split into two competing sets of agents. We focus on the solution concept of Nash equilibria (NE) We show that computing NE for this class of games is $textithard$ for the complexity class $mathrm$.
  arXiv  Detail & Related papers  (2021-11-07T21:15:35Z)
• Contextual Games: Multi-Agent Learning with Side Information [57.76996806603094]
  We formulate the novel class of contextual games driven by contextual information at each round. By means of kernel-based regularity assumptions, we model the correlation between different contexts and game outcomes. We propose a novel online (meta) algorithm that exploits such correlations to minimize the contextual regret of individual players.
  arXiv  Detail & Related papers  (2021-07-13T18:37:37Z)
• Learning to Play Sequential Games versus Unknown Opponents [93.8672371143881]
  We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents. Our results include algorithm's regret guarantees that depend on the regularity of the opponent's response.
  arXiv  Detail & Related papers  (2020-07-10T09:33:05Z)
• Metagame Autobalancing for Competitive Multiplayer Games [0.10499611180329801]
  We present a tool for balancing multi-player games during game design. Our approach requires a designer to construct an intuitive graphical representation of their meta-game target. We show the capabilities of this tool on examples inheriting from Rock-Paper-Scissors, and on a more complex asymmetric fighting game.
  arXiv  Detail & Related papers  (2020-06-08T08:55:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.