Evolutionary Adaptive Machine Intelligence - EAMI
An elegant machine intelligence described by David Heiserman in TRS-80 Color Basic.
The description of the algorithm, in totality, is broken-up into evolutionary complexities, called creatures. These creatures have an increasing complexity to the point that they are autonomous programs who learn by Reinforcement Learning I have encapsulated as Ideal. This code repository and evolutionary complexity serves as a foundational perspective on self-governing programs where the ultimate aspect is one that is homeostatic, or when is manifest as a presence. See US20180204107A1.
- ALPHA-I ReadMe
- Basic
- Scoring
- Navigation (maze)
- Strategy (paint)
- Confidence (compile)
- ALPHA-II ReadMe
- Basic
- Stratgy (feeding)
- Priority (nest)
- Confidence (compile)
- Track-Sense
- Multisense
- Sense-Response
- Duality (unbiased Anticipation data)
- BETA-I ReadMe
- Basic
- Scoring
- Confidence (decision seed)
- Scoring (memory)
- Confidence (memory-seed-compile)
- BETA-II ReadMe
- TBD
- GAMMA-I ReadMe
- General structure
- General template architecture
- Runtime presence
Multiplatform application versions of the sublime TRS80 emulator are in this folder. Extract the app for the platform and copy to the root directory of the various creature evolutions. The .gitignore file will keep this from being checked-in. Run a script in the following manner.
.\trs80gp experiments\alpha-1\ch-3\32-alpha1-basic.bas
And an emulator screen will appear.
Further:
.\trs80gp experiments\alpha1\ch-6\63-alpha1-compile.bas will ask for parameters, such as the number of cycles to run.
Then run the analysis.
Perform its intuitive-themed task.
And output the work results of the presence's experience as-per this particular experiment.
