AI Meta-Game Template for Human-Like Behavior
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Solution Overview
Problem
Current artificial intelligence in video games lacks the ability to understand and adapt to the meta-game, which is the style, strategy, and tactics employed by human players, resulting in a non-immersive experience as AI entities fail to mimic human behavior effectively.
Innovation Solution
A computer-implemented method that analyzes game histories to extract meta-game data, including team associations, location information, game chat, and item utilization, using natural language processing to create a meta-game template that can be applied in subsequent games to control AI entities, thereby enhancing their performance and human-like behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional AI scripts and macros are used to control non-player characters, then the AI can perform basic game actions, but the AI fails to understand and adapt to human player strategies and meta-game behavior
Solution Approach 1:
The system performs preliminary analysis of game histories and meta-game data before the actual game begins. By pre-processing and storing successful play patterns, team associations, location information, and chat contexts in advance, the AI is equipped with ready-to-use strategic knowledge that enables it to adapt to human player behaviors from the start of the game without needing to learn in real-time.
Solution Approach 2:
The system creates simplified representations (templates) of complex human player behaviors by analyzing game histories. These templates capture essential meta-game patterns, successful strategies, and contextual information that are copied and applied to control AI entities. This allows the AI to mimic human-like decision-making without requiring full complexity of actual human cognition.
2Reliability
If detailed meta-game data is collected and analyzed from game histories, then AI performance and human-like behavior improve, but the complexity of data processing and template creation increases
Solution Approach 1:
The system extracts only the most relevant and useful meta-game data from extensive game histories, filtering out unnecessary information. By selectively taking out key elements such as successful play patterns, team associations, location information, and contextual chat data, the system reduces processing complexity while maintaining high AI performance reliability through focused analysis of critical factors.
Solution Approach 2:
The system transforms raw, unstructured meta-game data into standardized templates with defined parameters and structures. By changing the parameters of data representation from raw game logs to structured templates with specific attributes, the system makes the data more manageable and easier to process, thereby reducing system complexity while improving reliability through consistent data formatting.
3Ease of manufacture
If AI entities use preprogrammed scripts for game actions, then the implementation is simple, but the AI behavior appears non-immersive and fails to mimic human players effectively
Solution Approach 1:
Instead of relying on manually programmed scripts, the AI system serves itself by automatically analyzing game histories and generating its own behavioral templates from actual human player data. This self-service approach allows the AI to develop adaptive, human-like behaviors without requiring complex manual programming, maintaining ease of implementation while significantly improving adaptability and immersion through data-driven behavior generation.
Data Source
AI summary
Control of artificial intelligence (AI) entities in a video game is enhanced using a meta-game template which reflects successful human player styles, strategies and tactics. Multiple game histories having game environment data and meta-game data relating to human behavior are analyzed, and specific meta-game data is statistically correlated with successful play. Meta-game data can include player chat, which is used to establish a context using natural language processing. Meta-game data can also include team associations, location information relating to a player, and item utilization by a player. Meta-game data can be associated with a particular time period of the game. A meta-game template is applied in a subsequent game by identifying a current context, matching the current context to the context associated with a template, and controlling an AI entity in accordance with the template. After conclusion of the game, the computer system presents meta-game statistics to a user.