Distributed Katago training (https://katagotraining.org/) has produced a new improved network a while ago. The network is known by nice name “b18c384nbt”. I installed the network to Hactar servers a few days ago, and they seem to work fine.
Networs are about 20% faster than old ones used in Hactar server. At the same time networks are stronger that old networks. So Hactar should be now both a bit faster and a bit stronger!
Improvement in the network is due to a new structure of the network. New network is still learning faster that old network was, so I will probably update the network once their learning speed has settled.
Hactar is finally retiring well-served Pachi engine for 19×19 boards. The Pachi engine cannot meet modern expectations, where AI easily beats humans. The 9×9 and 13×13 bots are still be Pachi-based, they will be converted to new AI engine later.
New engine is combination of katago engine and a custom model for human-level play. This allows the bot to play very human-like game.
The custom model is trained using human games. As most games were bit older, it mostly plays with somewhat older style. The model seems to be quite good at tactics and ladders. So even if it is weaker than katago, it is not easy to trick.
I will be adjusting the engine further in following days.
Let me know how the new engine feels!
Update: The new engine still bit bad with white and high handicaps. So Pachi is still doing the job for high handicaps.
During last 3 months I have pushed out series of improvements to Hactar GO. Main improvements:
Now I consider Ai analysis to be of production quality.
SGF editor screen layouts are better in almost all screen sizes.
Partial localizations have been improved, now most important texts should be there.
Board graphics are more readable.
Game score is cleaner and more readable.
Stability is a lot better now, ANR problems should be solved.
If you are interested in improving a localization or creating a new localization, some instructions are in https://gowrite.net/forum/viewtopic.php?t=898. To minimize typing, I have integrated DeepL and google translation engines. Their proposals can be used as a starting point. And don’t hesitate to contact me, I am happy to help!
For a while there was a bug in Hactar Go Lite which prevented use of Ai. This is now fixed and Ai is quite usable also without any subscription in Lite.
From this I will continue to make minor improvements to Ai and graphics. And in parallel I will continue to work for new, interesting features!
Hactar go version 3.0 introduces game analysis tool. Tool makes it easy to analyzing one’s games, or professional games, with any Android phone or tablet. As heavy computation is done in the cloud, the phone or tablet do not need to be high-end model for fast analysis.
Now the tool is available in most recent version of both Hactar Go and Hactar Go Lite. Please update your version!
Start analysis by selecting “Game Analysis” (or Ai icon) in SGF editor. If neither is visible, you probably have old Hactar Go version.
First whole game is analyzed using low iteration count. This offers overview to development of the game. It is also possible to move around in game using the analysis graph.
Red line is winrate, that is the probability that black would win the game. Black / White areas reflect the score difference between the players. In the middle the game is even. Each vertical line is 10 point advantage to the player. The text in the bottom gives precise numbers for these.
When visiting a move, Hactar makes more accurate analysis. The analysis shows loss of points compared to the best move (green). Smaller number is number of iterations used to analyze the move.
Selecting an analyzed position shows the best continuation, as if the move would be played.
Moves in variations are analyzed in the same way. Analysis starts visiting the move.
Details
Exact number of iterations for analyzing a move is still work-in-progress, and will be adjusted later (probably up). The actual load from real-world use is a factor, as GPUs are not free, and one-time payments may not be the best way to cover running costs forever.
Initially all pro subscriptions get 3000 evaluations in analysis, while Hactar Go users without subscription get 500 evaluations.
Hactar Go Lite with other than pro subscriptions get 1000 evaluations. With no subscription Hactar Go Lite users get 40 evaluations. 40 evaluation for a move is not much, but time will show if it is feasible to offer anything for free at all.
GPUs in Cloud
Analysis uses same GPUs in cloud as Hactar’s pro-level opponent. There are at least two machines serving the requests, both with multiple GPUs. This provides reasonably reliable and scalable back-end.
