Crypto-native Games

In accordance with gubsheep’s crypto gaming thesis, I propose two directions for crypto-native game development:

1. On-chain Generative Games

Example: an infinite on-chain grid-based dungeon where

• the game starts with a initial boundary expandable by player exploration;
• newly explored grid may contain object/monster which player can interact with;
• the spawning of object/monster and map-wide events are controlled purely by a generative algorithm that lives in the contract;
• rewards are given to player by the contract in the form of NFTs.

2. Autonomous Games.

Example: an autonomous chess contract where

• both EOA & contract are welcome to challenge;
• only one challenger at a time identified by address;
• if challenger does not make its next move for a predefined period of time (e.g. after X L1 blocks), the AI automatically wins;
• the chess board status is public at all time for query; every win/loss condition, a turn-by-turn record and challenger address is recorded in the contract.
• most critically, upon game contract deployment the private key is burnt so that the game is truly autonomous – no backdoor for any person/contract, which is to say the game is developer-free upon publication.

What really intrigues me is what the combination of the two directions above would yield. Example: an autonomous chess contract that auto-rebalances:

• following the autonomous chess contract above, with the addition of an auto-rebalance algorithm that adjusts the difficulty of AI according to the moving average of AI’s win rate over the past X games;
• the game would emit erc-20/erc-721 rewards to every winning challenger.

We can further give health point to the autonomous game contract. For example:

• following the auto-rebalancing autonomous chess contract, with the addition of a stake-to-challenge mechanism, where every challenger stakes Eth to begin challenge; a successful challenge would yield the challenger X amount of Eth from the contract’s balance, while an unsuccessful challenge would cost the challenger the Eth staked.
• the AI is reversibly dead when its balance goes to 0; it can be revived by infusion of Eth to its balance.

We can further give the autonomous game contract the ability to learn from past games:

• when the AI is tuned up to its highest difficulty and still loses more than it wins, it runs its learning algorithm on the recorded turn-by-turn game records to expands its difficulty spectrum;
• this would allow the game contract to extend its lifespan by winning more games and thus more Eth.

This idea coincides with my earlier thinking on The Autonomous Dimension. I believe we are helping to create a new species that live entirely on-chain - a tribute to Primavera De Filippi’s Plantoid project and the ArtDAO idea by Simon de la Rouviere and Trent McConaghy.