Solution and Product

Platform Overview:

Play AI provides a unified platform where multiple participants come together to create, train, and use AI agents. In simple terms, it’s a place where “AI meets gaming meets blockchain.” Users interact with AI agents through games and applications (dApps), the agents learn and improve via a powerful training engine, and all the important data (like the agents’ learned knowledge and rewards) is recorded on the blockchain for security and transparency. The end result is an ecosystem of ever-improving AI agents that anyone can own or utilize, and a rich database of decision-making data accessible to the community.

Types of Participants: The Play AI ecosystem is designed for four main participant groups, each with distinct roles and benefits:

Players (End Users): Players are individuals who own or train AI agents for fun or for competition. For example, a player can own an AI poker agent and train it to compete in Texas Hold’em games. Through an intuitive interface, players set goals or strategies for their AI (or even directly play against/with it) and watch it improve over time. Players earn rewards in tokens when their agents perform well or when they contribute valuable training data to the network. They can also trade or monetize their agents – since each agent is an NFT, a player could sell a well-trained AI on the open market. In short, players get entertainment, potential earnings, and the thrill of nurturing a personal AI. Even non-technical users can participate: the complex AI stuff happens under the hood, while they experience engaging gameplay and perhaps even “play-to-earn” opportunities.

Developers: Developers are the creative builders who expand the platform’s capabilities. Play AI offers a robust SDK (Software Development Kit) and API that developers can use to create new games, scenarios, or tools on top of the platform. For instance, a game developer might integrate Play AI agents into a new strategy game, or build a custom training environment (beyond poker) to teach agents different skills. Developers can also contribute new AI algorithms or model improvements to the community (for example, a better reinforcement learning strategy), which can be plugged into the training engine. In return, developers can monetize their contributions: they might charge a fee (in tokens) for access to a game they built, or receive token rewards when their environment is used by many agents. The platform’s open architecture ensures that third-party developers can seamlessly hook into our AI-agent system – analogous to building “apps” for an AI app store. This fosters a vibrant ecosystem of dApps and content, continually enriching what Play AI offers to users.

Companies & Institutions: Play AI isn’t just for individuals – companies, research labs, or even game studios can be part of the ecosystem. Companies can use the platform in multiple ways. A gaming company could utilize Play AI to enhance NPCs (non-player characters) in their games with smarter behavior, by leveraging our training engine and agent NFTs (instead of developing AI from scratch). A financial firm or a supply chain company might use the platform’s MCDM data layer to analyze decision strategies for their domain – for example, using patterns learned from game theory (poker strategies) to inform economic decision models. They could also train proprietary AI agents in a private or federated manner using our tools, benefiting from the collective knowledge base without exposing sensitive data. These enterprise users would pay for access to data or for tailored AI-agent services using the token. In exchange, they gain cost-effective AI solutions and access to a global talent pool of AI strategies. Moreover, forward-thinking companies might contribute data to the ecosystem (e.g. a dataset of real-world decisions) and stake tokens to support agents that align with their interests, thereby influencing development and earning rewards.

Data Providers (and Miners): Data providers are those who contribute raw materials that fuel AI training – whether it’s datasets, simulation environments, or computing power. For example, a data provider might supply a specialized dataset (like historical poker hands, or annotated decisions from expert gamers) into the Play AI network to improve agents’ learning. Alternatively, they might operate as compute providers (analogous to miners in blockchain): running nodes that provide GPU computing power to train the AI agents (our training engine can distribute tasks to such nodes). These providers ensure the system can scale – the more data and compute available, the more agents can be trained simultaneously and efficiently. In return, data providers earn token rewards for their contributions. The reward mechanism is similar to mining or staking: if you contribute valuable data that gets used by many, you receive tokens; if you run a training node, you’re paid in tokens for the work done (possibly with bonuses for high-quality, reliable service). This not only decentralizes the infrastructure (no single server farm controls the AI training) but also creates an incentive-driven supply chain for AI development. Over time, as the network grows, we expect independent data providers to compete and collaborate in curating the best data for training AI – creating a virtuous cycle of better data → better AI → higher demand for data.

User Scenarios and Initial Products: To make these roles more concrete, let’s walk through the first products and scenarios Play AI is bringing to market:

AI Poker Agent (Texas Hold’em Trainer): Our flagship pilot application is a Texas Hold’em poker training arena. In this scenario, a user (player) acquires an AI poker agent – represented by an NFT – and enters it into the Play AI poker arena. The AI agent starts with basic knowledge of poker rules and perhaps some fundamental strategy. The player’s goal is to train this agent to become a skilled poker player. Through an interactive dashboard, the player can watch matches (the AI will play against other AI agents or simulation opponents in Texas Hold’em) and provide guidance. For example, the user might adjust the agent’s risk preference (“be more aggressive on bluffs”) or choose from preset training modules (one module might focus on end-game strategy, another on reading opponent patterns). The training engine runs thousands of poker hand simulations, allowing the agents to learn by trial and error (a form of reinforcement learning). As games are played, the agent improves its win-rate, and all the decisions it makes (fold, call, raise, considering game context) are recorded as part of its learning log. This data is distilled into the agent’s MCDM knowledge base – essentially the agent learns which strategies lead to better outcomes under various criteria (opponent type, hand strength, pot odds, etc.). The blockchain comes into play by recording key achievements and data points: for instance, when an agent reaches a new skill level, that proof is stored on-chain; aggregated statistics or strategy profiles can be stored as well (possibly as IPFS hashes). The NFT representing the agent is dynamically updated with this progress – e.g., its metadata might now show “Skill Level: Pro” or contain a hash of the agent’s latest model parameters. From the user’s perspective, they now have a unique AI poker agent that they trained. They can use it in competitions (imagine community-run AI poker tournaments with prize pools) or they might even rent/sell the agent to someone else (for example, a new player might buy a veteran agent as a coach). This poker training product not only provides entertainment and potential earnings for users, but it’s also generating massive amounts of high-quality decision data. Every bluff, every risk calculation the agents make is valuable data for improving decision-making AI. Thus, the poker arena serves as both a game and a crowdsourced AI training laboratory. (Notably, games like poker are famously complex for AI – solutions like Libratus beat pros after enormous computation. Play AI opens this process so that a distributed community can tackle such complexity together.)

< The image of AI gaming agent’s evolution processing in dNFT >

Dynamic NFTs (dNFTs) – Living AI Assets: A cornerstone of the product is that each AI agent is tied to an NFT, but not a static one – a dynamic NFT that evolves. In Play AI, when we say dNFT, we mean an NFT whose properties can change based on the AI’s learning progress. How does this work? Under the hood, the NFT might contain references to the AI agent’s model parameters or skill metrics stored off-chain (for instance, on a decentralized storage like IPFS). After each training milestone, the platform updates the NFT’s metadata URI to point to the new model state or to update attributes. To illustrate, your AI agent NFT might initially have attributes like experience_level: 1 (Novice) and some base performance stats. As you train it through 1000 poker hands and it improves, the platform updates those attributes, say to experience_level: 2 (Apprentice) and higher stats. These changes are cryptographically signed by the platform’s smart contract to ensure authenticity. The NFT is “actual” in-game and on-chain evidence of your AI’s growth. Why do this as an NFT? Because NFTs give true ownership – you can hold it in your wallet outside the platform, trade it, or even use it in other compatible dApps. For example, a third-party might create a meta-game where your poker AI NFT competes in a bigger tournament across platforms – because the NFT is a standard token (ERC-721 or similar), it’s interoperable. We effectively treat the AI’s machine learning model as an owned asset . This dynamic NFT design is innovative but grounded in emerging practice – projects like Altered State Machine have pioneered the concept of “AI brains” as NFTs . Play AI extends this by focusing on decision-making quality (MCDM data) as the evolving trait. In simpler terms: think of your AI agent NFT like a Pokémon – it gains experience and “levels up” with training. But unlike a game character that only exists on a game company’s servers, your AI Pokémon is an asset you truly own on blockchain. This uniqueness fosters a player-driven economy: some players might specialize in training top-notch agents and then selling them; others might collect different agents with different skills. The NFT nature also ensures scarcity and provenance – if you see an agent NFT that claims to be “Master level,” you can trust that record because it’s on-chain and earned, not just claimed.

SDK/API & Future Scenarios: Beyond poker, Play AI’s SDK and API enable a world of scenarios. Our platform is built to be AI-game agnostic, meaning the same core system (AI agents + training engine + blockchain data layer) can power many types of applications. The SDK allows developers to define new environments and reward signals for agents. For instance, a developer could introduce a chess training module – using the SDK, they define how an agent plays chess, what constitutes a win or good move, and integrate it with the training engine. Suddenly, players could train AI chess grandmasters on Play AI. Similarly, one could add non-game scenarios: imagine an AI stock trading simulation where agents learn to trade stocks or crypto under certain risk constraints, generating financial decision data. Or a logistics simulation where agents learn to route deliveries (useful for supply chain decisions). The API also lets external applications query the Play AI network. For example, a web app could fetch an agent’s stats from the blockchain to display on a profile, or a research program could query aggregated decision data (with privacy preserved) to analyze how AI agents make choices under uncertainty. The key point: Play AI is a platform, not just a single game. While our first product is the Texas Hold’em AI trainer to showcase the concept, our roadmap (see below) includes expanding to multiple games and decision domains. The open SDK means the community can drive this expansion, suggesting and building the next training arenas. This ensures that Play AI’s product offering will continually grow, guided by what users and developers find most valuable.

By describing the platform in plain language and relatable scenarios, we see Play AI as “ready for market”: it’s not an abstract idea, but a tangible system where a user can come in today, train an AI poker agent, watch it improve, and benefit from the process. The combination of fun, learning, and earning is how we will attract and retain a strong community from day one.