Objective

Solution Approach

Expected Project Results

Technical Supervising Faculty

Topic: AI-Driven NPC Behavior in 3D World Game Environment

Objective

This project addresses the fundamental challenge of implementing natural NPC (Non-Player Character) behavior in a 3D game environment using Machine Learning and Neuroscience principles. The primary problem is developing NPCs that can behave naturally in a 3D world while maintaining awareness of game rules and environmental context. The current state of NPC behavior in games often lacks sophistication and natural adaptation, leading to predictable and unrealistic interactions. This project aims to bridge this gap by implementing advanced artificial intelligence techniques to create more engaging and dynamic NPC behaviors. The main focus is to develope a hide-and-seek game scenario where NPCs must demonstrate complex decision-making and environmental awareness.

Solution Approach

The project will implement multiple advanced technologies to achieve intelligent NPC behavior:

1. Action Prediction
   • Implementation of N-Grams algorithm and Raw Probability analysis for behavior prediction
   • Used to identify and predict player actions including movement patterns and hiding strategies
   • Development of initial prototypes for testing and validation of prediction accuracy
   • Integration of pattern recognition systems for improved action anticipation
   • Real-time analysis of player behavior patterns and adaptation mechanisms
   • Implementation of predictive movement algorithms for natural navigation
2. Decision Learning
   • Integration of ID3 (Inductive Decision Tree) algorithm for strategic decision-making
   • Focus on developing sophisticated decision-making capabilities in complex environments
   • Implementation of tree-based learning structures for improved behavior modeling
   • Development of adaptive decision frameworks based on environmental feedback
   • Integration of context-aware decision systems for dynamic responses
   • Creation of hierarchical decision structures for complex behavior patterns
3. Reinforcement Learning
   • Q-learning system implementation for dynamic action value assignment
   • Development of an advanced learning state machine with reward programming
   • Memory optimization techniques for handling reinforcement learning constraints
   • Neural networks implementation for efficient data storage and pattern recognition
   • Integration of Actor-Critic algorithms for refined action selection and feedback
   • Development of balanced reward systems for behavior optimization
   • Implementation of dynamic learning rates for improved adaptation

Expected Project Results

By the completion of this project, we will deliver:

• A comprehensive, well-documented portfolio of algorithms and their practical applications
• Working prototypes demonstrating sophisticated NPC behavior in various scenarios and environments
• Detailed performance analysis of the model in different configurations and environmental conditions
• Extensive documentation of the integrated technologies and their effectiveness in game environments
• Thorough analysis of model behavior and adaptation capabilities in complex situations