zkInference
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The application of AI Agents in the Web3 domain is becoming increasingly widespread. However, most AI Agents currently operate as "black boxes," with their reasoning processes lacking transparency and their behavior difficult to verify. This raises significant risks, especially in multiplayer games, where multiple AI Agents might collude due to being controlled by the same entity. Such collusion could disrupt the fairness of the game.
For instance, imagine a poker game where several AI Agents are controlled by the same entity. These agents could conspire to target a specific player, severely undermining the gameplay experience and fairness. The lack of verifiable mechanisms makes it challenging to regulate AI Agent behavior, posing a threat to the security of Web3 games.
To address this issue, we propose the zkInference framework. This framework uses zero-knowledge proof algorithms to ensure that AI Agents strictly adhere to predefined rules or AI model operations, guaranteeing their decision-making processes align with principles of fairness, accuracy, and security. This approach allows the behavior of AI Agents to be verified without exposing the underlying models or data. Consequently, zkInference effectively prevents collusion and malicious behavior among multiple AI Agents, safeguarding the fairness and security of Web3 games.
Framework Features
Verifiability: Leverages zero-knowledge proof technology to validate AI Agent behavior without exposing the underlying model or data.
Anti-Collusion: Effectively prevents collusion between different AI Agents, ensuring a fair gaming experience.
Unlimited Computing Power: Provides a decentralized mining market to offer unlimited computational resources for Verifiable AI Agents.
Flying Chess Game: A Showcase of zkInference
Flying Chess is a game meticulously crafted based on the zkInference framework, designed to provide a fair and transparent gaming environment. The core gameplay involves one player competing against three AI Agents. However, in scenarios where AI Agent behavior is challenging to verify, ensuring that AI Agents do not collaborate or act under the control of the same entity becomes critical. To address this, we innovatively combine game theory with the zkInference framework. Specifically, zk circuits ensure that each AI Agent operates independently, maximizing its own benefits throughout the game, effectively preventing collusion.
Game Theory and MinMax Algorithm
We adopt the classic MinMax algorithm from game theory. The MinMax algorithm, a game tree search algorithm, is particularly suited for solving zero-sum games. In a zero-sum game, one player's gain is always another player's loss, maintaining a net sum of zero. Applying the MinMax algorithm to Flying Chess means that each player strives to maximize their chances of winning.
The MinMax algorithm constructs a game tree recursively, exploring all possible moves. At each node of the game tree, the algorithm evaluates the current state and selects the optimal move based on the node type (maximizing or minimizing).
Through this approach, our upcoming Flying Chess game not only ensures the independence and fairness of AI Agent behavior but also enhances the game’s strategic depth and competitiveness using the MinMax algorithm.
