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AI running on the Microbee - zbfairlane - 15-07-2026

Hi all,

With all of the talk about AI lately, I wanted to prove that machine learning was not a new concept. My question, can I build a learning algorithm on 40 year old hardware (emulated).

It started out with a perceptron, which was a little underwhelming. 

Then I settled on build a maze solver. A simple problem, the bee needs to find the exit. with no prior knowledge of its map, the bee uses Q-learning to learn the pathway to the exit. I have programmed imperfection into this so it always will choose to explore a little even if it has mastered the map. 

I have wasted a lot of time on this, and it works, so i would love to share. If anyone would like to try it out the code is in the .txt down below the images, or if someone want to tell me how to get cp/m tools working on my mac where I wrote this I'll be happy to share the .mwb file. 

I am especially interested in seeing this run on actual old hardware, though its written for premium 512 becuase it uses HIRES2.

   
   
   


RE: AI running on the Microbee - ChickenMan - 16-07-2026

Thanks for that, I will give it a run tomorrow.


RE: AI running on the Microbee - Graham72 - 16-07-2026

    Good one. It runs on my vibecoded emulator. I exported the .mwb and also ran it in UBEE512. I have emailed the .mwb to CM. cheers g


RE: AI running on the Microbee - ChickenMan - 16-07-2026

Thanks, yes looking it even runs in Nanowasp

   


RE: AI running on the Microbee - zbfairlane - 16-07-2026

Thank you for sending those nice screenshots! I am very happy to see that. I will be giving it a run on my premium plus soon.


RE: AI running on the Microbee - Graham72 - 31-07-2026

an amended version 12x12 grid and some code revision.


RE: AI running on the Microbee - zbfairlane - 03-08-2026

It looks pretty good with the bigger map, and the auto run adds a different touch, It given me some more ideas.


RE: AI running on the Microbee - Graham72 - 07-10-2026

This may be of interest to you. A 9x9 Go! game that has strong AI, on the Sinclair Spectrum.

A search tree. The imaginary games are not independent: the computer grows a tree of the most promising lines and spends more games on them (Monte Carlo tree search). Every imaginary game also teaches it something about every move played in it (RAVE).

Smart imaginary games. They are not random flailing: they capture and escape ataris, follow good local shapes and avoid self-atari.

A sense of shape. Its first guesses come from 1,028 shape patterns and 45 tactical features packed into 4.4 KB. We trained them on 233,000 positions
evaluated by KataGo, one of the strongest Go programs in the world.

Ladders. Before it runs away with a string in atari, it reads the ladder to the end, and if the ladder still catches the string, it lets it go.

An opening library of 300 positions, gathered from 600 training games.

Thinking on your time. While you think, it thinks too, and what it finds is reused for its reply.

A smart clock. When the best move is clear it plays quickly, and the time saved goes to the hard moves.

A proper ending. When both players pass, it estimates which stones are dead with another 32 imaginary games, shows the territory and counts the score.

https://lawlord.itch.io/go-9x9?fbclid


RE: AI running on the Microbee - zbfairlane - 12-10-2026

I will take a closer look at this it looks reall yinteresting. Thank you