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This project does not involve machine learning. If anything, its development might be called “machine teaching”. I know how to play through Castlevania. And the challenge was to capture my knowledge into a computer program. The result is a system that simulates the same decision-making process that I perform when I have a controller in hand. Creating it involved articulating in elaborate detail the physics that govern Simon Belmont’s 8‑bit world and all the tactics required to be an expert vampire killer.

CastlevaniaBot has access to a catalog of strategies for handling a wide range of situations. Most of them are designed to handle a particular game object type. For instance, there is a strategy for dealing with skeletons, another for fishmen, another for whipping candles, another for collecting hearts, and so on.

CastlevaniaBot constantly monitors the game state and it switches between the available strategies as it deems necessary. The decision process uses a fitness function to rank all the onscreen game objects. The top rank is the primary target and when the primary target changes, it switches strategies. For example, CastlevaniaBot might be about to strike some candles when a bat flies into frame. Depending on how close it is to the bat, CastlevaniaBot may respond by switching from the candles strategy to the bat strategy. After whipping the bat, it can resume handling the candles.
nintendo  nintendo-entertainment-system  castlevania  artificial-intelligence  bots  emulation 
4 days ago by jbrennan
Anbox - Android in a Box
Anbox puts the Android operating system into a container, abstracts hardware access and integrates core system services into a GNU/Linux system. Every Android application will be integrated with your operating system like any other native application.
android  emulation  crossplatform  development  opensource  container 
12 days ago by cyberchucktx

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