Game Balance Studio

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Game Balance Studio | 遊戲平衡工作室

Game Balance Studio

Interactive tools for balancing fun and challenge in 3D game development

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Balance Theory Dashboard

📊 Flow State Analyzer

Boredom
Flow
Anxiety
75 Engagement
68 Retention
82 Satisfaction

📈 Difficulty Curve Designer

Click to design your difficulty curve. Try different patterns and see real-time analysis.
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3D Game Balance Simulator

🏃 Platform Challenge Balance

3D Platformer Simulation - Adjust parameters to see real-time balance changes

Jump Mechanics

Platform Layout

Challenge Factors

Adjust the platform parameters above to see real-time balance analysis and recommendations.

🧩 Puzzle Complexity Balancer

Interactive Puzzle Design - Create and test puzzle mechanics

Puzzle Elements

Hint System

⚔️ Combat Balance Optimizer

Combat Simulation - Balance player vs enemy stats

Player Stats

Enemy Stats

65% Win Chance
45s Fight Duration
7.2 Difficulty

🗺️ Exploration Pacing Designer

Open World Exploration - Design discovery and reward pacing

World Density

Reward Distribution

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AI Balance Advisor

🔍 Game Analysis

Click 'Analyze Current Setup' to get AI-powered balance recommendations based on your current game parameters.

📋 Design Checklist

Design Completion: 0%
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Case Studies & Best Practices

🍄 Super Mario Series

Perfect progression curve with intuitive controls and forgiving mechanics that gradually introduce complexity.

🧟 Resident Evil 4

Dynamic difficulty adjustment that responds to player performance without breaking immersion.

🏹 The Legend of Zelda

Masterful balance of exploration freedom with guided discovery through environmental design.

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Export & Integration

💾 Project Export Tools

Export Formats

Integration Code

Select an export format to generate integration code for your game engine.

此網誌的熱門文章

自訂網路結構的神經網路訓練與預測 (動畫＋公式＋損失／激活函數

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自訂網路結構的神經網路訓練與預測 (動畫＋公式＋損失／激活函數) 自訂網路結構的神經網路訓練與預測 此範例示範一個多層神經網路，其網路結構、激活函數及損失函數可依需求自訂。 訓練與預測均以動畫展示前向傳播（並於訓練時進行反向傳播更新權重），搭配公式與統計資訊。 深入了解神經網路的基本元素與計算方法 在本範例中，我們介紹神經網路中的各個核心基本元素，如參數與超參數，並說明它們如何在模型計算與實際應用中發揮作用。 參數 (Parameters) 參數主要指神經網路中的權重 (Weights) 與偏置 (Biases)。在前向傳播時， 輸入資料依據公式： a = activation(Σ(x * weight) + bias) 其中，x 為輸入值、weight 為權重、bias 為偏置，activation 則代表激活函數，用以引入非線性。權重與偏置在訓練過程中透過反向傳播 (Backpropagation) 依據損失函數的梯度進行更新，以達到模型調整誤差的效果。 超參數 (Hyperparameters) 超參數是在訓練前由使用者設定的參數，不會在訓練過程中自動調整。常見超參數包括： 學習率 (Learning Rate)： 控制每次參數更新的步幅，影響收斂速度與穩定性。 Epoch 次數： 表示整個資料集被完整遍歷的次數，決定訓練強度與時間。 網路結構設定： 包含輸入層、隱藏層與輸出層神經元的數量，直接決定模型表達能力。 激活函數與損失函數： 分別決定每層的非線性處理與模型誤差的計算方式。 合理配置超參數能夠有效避免過擬合或欠擬合，並確保網路運算的穩定與高效。舉例來說，若學習率過高，可能會使參數更新過度，導致模型難以收斂；相反，學習率過低時則會使訓練變慢。 基本原理與實際應用 神經網路的運算流程主要分為前向傳播與反向傳播兩大步驟： ...
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Customizable Neural Network Training and Prediction (Animations + Formulas + Loss/Activation Functions)

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Customizable Neural Network Training and Prediction (Animations + Formulas + Loss/Activation Functions) Customizable Neural Network Training and Prediction This example demonstrates a multi-layer neural network where you can customize the network structure, activation functions, and loss functions as needed. Both training and prediction are shown with animations illustrating forward propagation (with back propagation update during training), accompanied by formulas and statistical information. In-depth Understanding of Neural Network Basic Elements and Calculation Methods In this example, we introduce the core components of a neural network—parameters and hyperparameters—and explain how they function in model computation and practical applications. Parameters Parameters refer mainly to the weights and biases within the network. During forward propagation, input data is computed as: a = activa...
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