Architecture (High-Level)

At a high level, the Play AI architecture consists of two core layers with a user-facing interface on top:

1. Algorithm Engine Layer (AI Training Engine): This is the “brain” of the platform where all AI learning happens. It includes:

AI Agent Models: The underlying algorithms (reinforcement learning models, neural networks, etc.) that define how agents learn and make decisions. These can be single-agent or multi-agent models, and we support various libraries (for example, TensorFlow or PyTorch based models for deep learning, as well as custom MCDM algorithms).

Environment Simulation: The sandbox where agents operate. For Texas Hold’em, the environment simulates poker games; for other tasks, it simulates that task’s world. It provides the state (inputs) to the AI agents and receives their actions.

Training Orchestrator: A high-performance training engine that runs the simulations and training cycles at scale. This includes distributed computing clusters, cloud or edge compute providers that run training tasks (similar to how mining nodes run computations in blockchain, our “training nodes” run AI computations). The orchestrator manages scheduling, parallel training of many agents, and ensures efficient use of computational resources (including heterogeneous compute like GPUs).

Data Pipeline: As agents learn, a flood of data is generated (game states, decisions, rewards, model updates). The algorithm layer processes and filters this into meaningful MCDM data records. For example, it might aggregate how often a certain strategy was successful in poker when multiple criteria were at play (good hand but high risk, etc.). This pipeline prepares data to be handed off to the blockchain layer.

Developer Interface (SDK/API): As part of this layer, we provide tools (SDKs) for developers to plug in new environments or algorithms. Also, monitoring tools like a web dashboard to visualize training status, agent performance metrics, etc., live here to give feedback to users. This includes real-time visualization of an agent’s learning progress or match outcomes.

2. Blockchain Layer: This is the “heart” that provides security, ownership, and coordination across the platform. It consists of:

Smart Contracts: These govern the creation and management of agent NFTs, reward distribution, and token transactions. For example, when an agent NFT is minted for a new user, a smart contract handles that. When training is completed and needs to update an NFT’s metadata (like leveling up), a contract call is made (only authorized by our protocol to maintain integrity). Smart contracts also manage the token economy – like distributing token rewards to participants (players, developers, data providers) based on predefined rules.

Data Ledger: The blockchain acts as a decentralized ledger for key AI data. We do not store every byte of raw training data on-chain (that would be impractical for speed and cost), but we store essential data and hashes. For instance, final model parameters or important decision logs might be stored via a hash reference (with the bulk data in a decentralized storage such as IPFS or Arweave). The chain thus contains a tamper-proof record of AI knowledge – anyone can verify that a given agent’s skill claims are backed by an immutable record. It’s a transparent MCDM data layer: e.g., a contract might record “Agent X win-rate in high-risk decisions: 70%” after training, and this is visible publicly. Having this on-chain means the community can audit how agents are improving and how rewards are determined, ensuring fairness.

Token Transactions: The blockchain layer, of course, handles all token transfers. When a player pays tokens to start a training session or to enter a tournament, that transaction is on-chain. When a developer receives a reward from the ecosystem fund for contributing a popular environment, it’s through an on-chain transaction. This provides transparency in the economy – everyone sees where tokens flow, reinforcing trust that, for example, the reward system is fair and not arbitrary.

Governance Mechanisms: Embedded in this layer are governance modules (possibly a DAO contract) that allow token holders to vote on proposals (we’ll detail governance in a later section). The outcomes of votes – such as approving a use of treasury funds or changing a parameter of the reward formula – are executed by smart contracts, ensuring the platform evolves according to community consensus.

3. User Interface Layer (dApps): On top of these layers are the decentralized applications that users actually interact with. This includes:

Web/App Interface: A user-friendly application (web dashboard and in the future mobile app) where players log in (likely via a crypto wallet), manage their AI agents, start training sessions, watch live simulations, and interact with other users (community features, sharing results, etc.). It communicates with the backend via the API and blockchain calls. For most users, this interface abstracts the complexity – they see a game or an AI pet they train, without needing to know about blockchain transactions happening in the background (though transparency is there for those who want to dive deeper).

Marketplace: A built-in NFT marketplace dApp where agents (NFTs) and possibly data packs or other assets can be listed for sale/trade. This marketplace uses smart contracts to facilitate sales and auctions in a trustless way. Users can browse AI agents by their skills, history, owners, etc., and buy one using the token. Similarly, developers could list new environment modules or upgrades if applicable.

Community Hubs: Likely integrated forums, chat, or social features (could be off-chain but accessible via the platform) to allow collaboration – e.g., players can form training leagues, or data providers can discuss which data is needed next. While not strictly part of the on-chain architecture, these features are important for a thriving community-driven ecosystem and will be part of the product offering.

Diagram (Conceptual): Imagine a simplified flow: a user interacts with the AI Agent (via the dApp) → the agent’s actions and learning are processed by the Training Engine (algorithm layer) → important results get recorded on the Blockchain (blockchain layer) → which in turn updates the agent’s NFT and triggers any token rewards. Meanwhile, developers and data providers plug into the system at the algorithm layer (via SDK) and blockchain layer (via smart contracts for rewards), enriching the cycle. And all of this runs in a decentralized manner, where multiple nodes (for compute and for blockchain consensus) ensure no single party controls the outcomes.

This architecture ensures scalability and security. By separating concerns (AI computation off-chain where it can scale, and critical records on-chain where they are secure), we get the best of both worlds: high-performance AI training and the trust guarantees of blockchain . The use of blockchain also means that the Play AI platform is not just a closed system – it’s interoperable with the broader Web3 ecosystem. For example, our agent NFTs could be recognized by other metaverse projects, or external blockchain oracles could feed real-world data into our training if needed.

Finally, for those interested in the deep technical details – such as the mathematical foundations of our MCDM algorithms, the design of our distributed training clusters, or the specifics of how Texas Hold’em is implemented for AI learning – please refer to the Technical Appendix (or our separate technical paper). In that document, we provide extensive information on the AI models (including references to the history of MCDM in AI, from early decision theory by Franklin and Raiffa to modern reinforcement learning) and the technical choices we’ve made. The high-level architecture above is meant to give a clear picture without diving into code or formulas. Now, let’s move on to the token that powers this ecosystem and how we ensure it is used in a sustainable, value-generating way.

Token Design and Tokenomics

ZINO is the native utility token of the Play AI ecosystem. It is deployed on BNB Smart Chain (BSC) with a fixed total supply of 3 000 000 000 ZINO. The supply is minted once and governed by smart contracts, with no additional inflation beyond the predefined vesting schedules.

At Token Generation Event (TGE), the initial price of ZINO is set at 0.006 USD. The initial circulating supply is 240 000 000 ZINO, which corresponds to 8% of the total supply. This implies an initial circulating market capitalization of 1.44M USD and a fully diluted valuation (FDV) of 18M USD.

Token allocation is structured as follows:

• Community – 35% (1 050 000 000 ZINO)

10% of the Community allocation unlocks at TGE (3.5% of total supply). The remaining tokens are released over 36 months, with monthly emissions aligned to real usage: rewards for players, contributors, and data providers who grow the ecosystem.

• Ecosystem – 28% (840 000 000 ZINO)

10% of the Ecosystem allocation unlocks at TGE (2.8% of total supply). The rest vests over 60 months. This pool funds integrations, partnerships, liquidity programs and long-term ecosystem incentives. It is gradually deployed to support real product growth rather than short-term speculation.

• Investor – 17% (510 000 000 ZINO)

10% of the Investor allocation unlocks at TGE (1.7% of total supply). The remaining 90% is subject to a 12-month cliff and then linearly vests over 48 months. This structure rewards early backers but prevents excessive sell pressure in the first years of the project.

• Foundation – 10% (300 000 000 ZINO)

No tokens from the Foundation pool unlock at TGE. This allocation has a 12-month cliff and then vests over 48 months. It is reserved for long-term strategic initiatives, research and development, and resilience in adverse market conditions.

• Team – 10% (300 000 000 ZINO)

Team tokens are fully locked at TGE. They follow the same schedule as the Foundation pool: 12-month cliff, then linear vesting over 48 months. This directly aligns the core team with the long-term success of Play AI and removes the risk of early token dumping.

In total, 8% of the supply is in circulation at TGE through the initial unlocks of Community, Ecosystem and Investor allocations. The remaining 92% is locked and released over time according to the vesting schedules above. This model combines early liquidity for the market with a predictable, multi-year unlock curve that supports sustainable growth instead of short-term pumps.

The large share allocated to Community and Ecosystem (63% combined) ensures that ZINO gradually flows to users, builders and partners that actively develop Play AI. Investor, Foundation and Team allocations are sized and structured to provide capital, governance continuity and execution capacity without concentrating control in a small group.

ZINO will be used across the platform for access to AI agents and data, participation in dNFT-based games, staking and validation processes, and governance. The detailed description of utilities and their link to demand for the token is provided in the following subsections of the whitepaper.

Token Utility: The ZINO token has several key utilities in the Play AI ecosystem, making it a true utility token rather than a passive asset:

Payments for Access to Data and Agents: If a user or a company wants to utilize the platform’s AI resources, they will pay in ZINO tokens. For example, a player might spend tokens to unlock advanced training modules for their AI agent, or to enter a premium tournament with higher rewards. A company might subscribe to a data feed (say, aggregated decision insights from the platform) by paying tokens, or pay to use a large number of training hours on our engine for their custom agent. Essentially, any service or product in the Play AI marketplace – whether it’s an AI model, a dataset, or computational power – is transacted in ZINO tokens. This creates constant demand for the token as the platform’s usage grows . It also means revenue for service providers: those who contribute these services (like someone offering a dataset) receive tokens as payment, aligning the economy internally.

Staking for Data Validation and Quality Assurance: We will implement a staking mechanism to uphold data and AI model quality. Data providers (or even AI model providers) may be required to stake tokens as a deposit when they submit data or new AI agents to the network. For instance, if someone wants to contribute a dataset of gameplay logs for others to train on, they put up a stake in tokens. If the data is valid and proves useful (no one challenges it, and agents benefit from it), the provider can earn additional tokens as a reward over time (almost like earning interest or a curators’ reward ). However, if the data is found to be erroneous, low-quality, or malicious (e.g., mislabeled or spam), the stake can be slashed (taken away) by the protocol or via a community validation process, to disincentivize bad data. Similarly, when our training nodes perform computations, they might need to stake to ensure they behave honestly (not faking results) – if they do the job right, they get their stake plus reward; if not, they lose some stake. Staking thus provides a trust layer: it aligns incentives so that participants maintain high quality, because they have “skin in the game.” For end users, we could also have staking-based features: e.g., a user might stake tokens on their own agent as a signal of its quality or to earn passive income if their agent’s strategy is copied by others (this is analogous to SingularityNET’s idea of staking to signal good AI agents ). Overall, staking introduces a healthy gamification and reliability mechanism: the best contributors get more tokens, the worst get penalized, thus improving the ecosystem’s data layer.

In-Game Currency for Gaming and dNFTs: Within the Play AI platform’s gaming aspect, the token acts as the in-game currency. For example:

In the poker arena, players might have to pay an entry fee in tokens for tournaments (which then gets redistributed as prizes).

If there are customization options for your AI agent (imagine you could buy a special “skin” or appearance for your AI avatar, or power-ups that let the agent train faster for a short period), those would be bought in tokens.

Trading of agent NFTs between players uses the token as the medium of exchange (thus NFT marketplace transactions drive token usage).

Perhaps in future games, there will be betting or challenges (like “I bet 100 tokens my AI can beat yours in a head-to-head match”) – those wagers would naturally use the platform token.

If we introduce dNFT breeding or combining (for example, merging two trained agents to create a new one, or upgrading an agent’s rarity), tokens could be required to perform those actions (this mechanism is common in NFT games to control supply and add utility).

So the token essentially powers the game economy. As more players join and engage in these fun activities, the velocity of tokens increases. Importantly, we can decide to implement token sinks: some portion of tokens spent in-game could be burned (permanently removed from supply) or sent to the treasury. For instance, a 5% marketplace fee on NFT sales might be burned to support token value (like how Axie Infinity or other P2E games designed fee burns). We’ll calibrate these to balance user friendliness with tokenomics strength.

Governance Rights: The ZINO token will grant holders the ability to participate in governance of the platform. In practice, this means token holders can vote on proposals or even parameter changes. Some examples of what governance may decide:

Allocation of funds from the Ecosystem/Treasury pool (e.g., vote on spending X tokens on a marketing campaign or a developer grant).

Updates to platform parameters, like reward rates, staking requirements, or addition of new game modules.

Election of council members or core contributors (if we set up a community council or DAO committee, token holders might vote on who sits on it).

Eventually, governance could even influence the AI agents’ ethical boundaries or policies (for instance, a vote to disallow agents being used for certain malicious purposes – although our primary domain is gaming, this is a nod to ethical AI).

The governance model could start as a simple token-weighted voting (1 token = 1 vote) using an established framework (like Snapshot for off-chain signaling or on-chain via our own contracts). We might implement quadratic voting or other measures in the future to prevent plutocracy, but initially, token weight will be key. Crucially, active participation will be encouraged possibly by staking for governance (some DAO require you to lock tokens to vote, to ensure commitment). The large community allocation means many users will have some tokens, hopefully decentralizing the votes. We are aware that governance needs active community, so alongside just giving the mechanism, we will proactively foster discussion (via forums, Discord, etc.) for proposals. The token’s role in governance ensures that the platform can truly become community-driven over time. Token holders have a direct say, effectively making them akin to shareholders of a decentralized network (though legally it’s a utility token, not equity – see Legal section). This gives the community confidence that as they invest time and effort into Play AI, they also have a voice in its future direction.

In summary, the ZINO token is deeply integrated into every aspect of Play AI. From using services, to securing the network’s data quality, to gameplay and governance – the token is the thread that weaves everything together. This multi-faceted utility is by design: tokens that have singular use (or no clear use) tend to flounder, whereas tokens that are demanded by many activities tend to thrive as the platform grows . By giving ZINO real jobs in the ecosystem, we ensure it will be in constant circulation and high regard. And by tying it to governance and staking, we encourage holders to think long-term and engage actively (rather than just speculate passively). The result should be an economy where users, developers, and token holders are all aligned – everyone wants to increase the platform’s usage and value, which in turn increases the token’s value, completing a positive feedback loop.