AI Arena is not just a game that combines AI, but also a platform for cultivating players' AI abilities. With the advent of the AI era, training a suitable AI assistant has become a necessary skill in people's work and life, and an important indicator of employees' work capabilities in the workplace.

The combination of AI and games allows players to improve a certain soft skill while entertaining and relaxing. AI Arena has made a bold attempt in this regard and found a suitable entry point. In the future, as more and more players master the ability to train AI assistants, AI Arena can also provide a bilateral trading market based on protecting the intellectual property of AI practitioners and facilitate transactions between buyers and sellers.

AI Arena, developed by ArenaX Labs, has announced the completion of a new round of financing of $6 million, with Framework Ventures leading the investment and SevenX Ventures, FunPlus/Xterio, and Moore Strategic Ventures participating. The company plans to use this funding to build a PvP combat platform and develop similar games.

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AI Native Product Analysis

AI Arena

1. Product: AI Arena

2. Founder: AI Arena is developed by its parent company ArenaX Labs, which was founded in 2018 by three founders (Brandon Da Silva, Dylan Pereira, and Wei Xie), dedicated to creating independent games.

3. Product Introduction:

AI Arena is an Ethereum-native game where players from around the world can buy, train, and battle NFT characters driven by artificial intelligence. It is a platform tokenizing NFTs supported by real AI. In the game, players design and train AI-driven NFT combat characters for global PvP arena matches and let these characters battle automatically, with the ultimate goal of knocking opponents off the platform. Players help AI characters progress through a process called "imitation learning," where they learn skills by observing human behavior. In turn, players can evaluate AI's abilities through "AI Inspector" and identify its weaknesses as a focus for future training.

4. Development Story:

• Completed a $5 million seed round of financing in October 2021, led by Paradigm with participation from Framework Ventures.
• Completed a new round of financing of $6 million in January 2024, with Framework Ventures leading the investment and SevenX Ventures, FunPlus/Xterio, and Moore Strategic Ventures participating.

01. AI Arena Product Vision

Brandon Da Silva, CEO of AI Arena's parent company ArenaX Labs, worked for 5 years at OPTrust, Canada's largest pension fund, integrating machine learning into investment analysis as the main theme of his career. Brandon explained on his Twitter why he decided to create AI Arena - to lower the threshold of the AI industry, allowing all AI enthusiasts to showcase their abilities without being constrained by qualifications, to realize the dream of technical personnel fully owning the fruits of their labor through NFTs carrying AI models, and to attract interest in AI in a more fun way during gameplay. These three goals form the value flywheel of AI Arena. In the long run, AI Arena will create a bilateral market for AI based on the gaming platform, aiming to protect the intellectual property of AI practitioners and help them monetize their work, facilitating transactions between buyers and sellers.

02. How AI Arena Combines with AI

AI Arena, although a fighting game similar to Nintendo's Super Smash Bros. and Street Fighter, is also a project involving multiple intersecting fields: AI/ML, encryption, gaming, and NFT. One important distinction from other fighting games is that players cannot control the "boxers" they own.

So how do they fight?

The boxers are powered by AI, which tells them what actions to take in certain situations. Each boxer has a different AI, so whether your boxer can be trained to become a champion depends entirely on the player.

You can think of this game as guiding a boxer preparing for a fight. Players can upgrade by configuring their training programs or engaging in real combat, so that the boxer learns to replicate their actions.

Why use neural networks?

In simple terms, neural networks mean that theoretically it can learn the mapping of any user action. To enable the boxer to use neural network learning strategies, AI Arena will adopt simulation learning and reinforcement learning, with the neural network architecture stored on the InterPlanetary File System (IPFS).

The connections between neurons become "weights." When your neural network is "learning," it is changing the values of these weights. The weights ultimately determine how states map to actions, meaning we can interpret weights as "intelligence." The neural network weights are unique for each NFT and stored on the Ethereum blockchain.

Training the boxer is the process of changing the weights in the neural network to enable the AI to function. For example, if we are in front of an opponent, we may want our boxer to take the initiative. There is a series of weights that can achieve this, and the focus of training is to teach the AI to take specific actions in specific scenarios.

AI Arena embeds the following training programs in the application:

(1) Imitation Learning

The best way to understand imitation learning is to imagine yourself as a master and your AI as a boxer you are preparing for battle. You use your artificial intelligence for combat, and it learns to imitate your actions in specific scenarios.

By actual demonstration, you can test some actions and observe how AI imitates you. Please note: it will not immediately replicate your actions because the neural network needs some time to learn, so you may need to repeat your actions multiple times before the AI learns.

(2) Self-learning

The perfect boxing partner is yourself. Through self-learning, your AI is always challenging itself and constantly improving. In self-learning, AI learning and fighting like an opponent have little meaning because the opponent is a clone of the artificial intelligence itself. But if there are no experts to show AI how to fight, how does it learn what to do? - Through rewards. AI will learn to take actions that give it more positive rewards and reduce actions that give it negative rewards.

Of course, AI Arena repeatedly emphasizes its concern for providing equal opportunities to everyone - the team hopes that rewards will be given more to users who persist in training AI, rather than rewarding users with more resources.

03. An Analysis of the Innovative Path of Integrating Games with AI

In the current booming field of Artificial General Intelligence (AGI) technology, large language models (LLMs) are the absolute protagonists. With more and more teams investing in developing AI-Agent systems driven by LLMs, it is possible for AI Agents to redefine the innovative path of Web3 games. For example, the game "The Sims" uses LLM technology to generate 25 virtual characters, each controlled by an Agent supported by LLM, living and interacting in a sandbox environment.

The design of Generative Agents is very clever, combining LLM with memory, planning, and reflection functions, allowing the Agent program to make decisions based on previous experiences and interact with other Agents. This game demonstrates the capabilities of AI Agents, such as generating new social behaviors, information dissemination, relationship memory (e.g., two virtual characters continuing a discussion), and coordinating social activities (e.g., hosting a party and inviting other virtual characters). In short, AI-Agent is a very interesting tool, and its application in games is worth exploring in depth.

Although there have been various attempts to apply AI in the Web3 gaming field, the most mature application recognized in the Web3 gaming track is NFT Agents. In the future, NFTs will definitely be an important part of Web3 games. With the development of metadata management technology in the Ethereum ecosystem, programmable dynamic NFTs have emerged. For NFT creators, they can make NFTs more flexible through algorithms. For users, there can be more interaction between users and NFTs, and the generated interaction data becomes a source of information. AI Agents can optimize the interaction process and expand the application scenarios of interaction data, injecting more innovation and value into the NFT ecosystem.

As mentioned earlier, AI Arena is the world's first battle game that combines AI and NFTs. Users can continuously train their battle spirits (NFTs) using LLM models and then send the trained battle spirits to PvP/PvE battlefields. The battle mode is similar to Nintendo's Super Smash Bros, but with added competitive fun through AI training.

In conclusion, the integration of games and AI can not only solve the problem of sacrificing user experience for security and decentralization in Web3 games, but it is also likely to be the first application scenario where AI is implemented and achieves a larger user base.

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