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- Define
Define a bug and start the bot in a known and safe position in the level. - Let bots play
One or more bots explore the level. - Review
The bot logs when it encounters the bug along with everything it did to get there. - Continuous Updates
Developers and QA continuously receive reports on the bot’s progress.
- Record
Record the sequence of actions you wish to test by playing them. - Train
The bot trains on the recording. It learns to play with enough flexibility to allow variations in the Engine & Game Design. - Replay
Start the automatic replay cycles. The bot will report if a replay fails. - Continuous Updates
Continuously receive status updates on the bot’s performance.
- Input
Input existing hand-made levels (as little as ~100) to train the level generator & evaluation bot. - Define
Set the desired criteria for new levels & let the system generate & evaluate automatically. - Review
Review the output & select levels for final polish. - Repeat
Enrich the training set with new levels.
- Integrate
Integrate modl.ai plugin into your system. - Collect
Collect player data from play sessions with real players. - Train
The bot trains on the collected data to adopt behavior & skills from the human players. - Integrate
Integrate the bot into the game. Update the bot with new player data as the player base and game evolves.
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What AI methods is modl.ai using?
Our team has a broad background in AI and machine learning. We know it is essential that the development tools we provide are stable and cost-efficient. So we apply the AI methods that make sense for the use case. A finite state machine gets the job done in some situations. For other tasks, reinforcement learning is the right approach.
How much data is needed for training?
How much data is needed depends on the complexity of the game and the service. Some of our services, such as the Glitch Finder testing bot, work without training on data. Others rely on machine learning and therefore require data.
Our puzzle level generation and evaluation tool, Match Maker, can be kickstarted with 15-25 levels per game mode to train both the bot and generator.
For bots that replicate player behavior, you can start with data from your regular playtests. Depending on the game, a handful of play sessions can be enough to train the bots. As the game evolves, you can update the bot with additional data.
Which game engines does modl.ai's solutions work on?
We have plugins for the Unreal and Unity engines. However, integration with any custom engine is possible.
What will a test report from bot include?
In addition to reporting on detected issues or events, a test report can also include information about the game state, the bot's position, and the bot's actions. As you configure the bot, you can decide what is meaningful to track in your game.