Closed dawackyman closed 3 weeks ago
Adam-Kulju
commented
3 weeks ago If you lower Patricia's skill level, she doesn't search for less time; instead, she searches for the normal amount of time, then intentionally picks a suboptimal move based on how bad it is compared to the "best" move.
dawackyman
commented
3 weeks ago If you lower Patricia's skill level, she doesn't search for less time; instead, she searches for the normal amount of time, then intentionally picks a suboptimal move based on how bad it is compared to the "best" move.
I see, thanks for the clarification. Do you plan to improve Patricia in this specific way? With engines like Rybka232, limiting the strength automatically reduces the search depth, but with Patricia, lowering her skill level actually seems to slow her down. Will future updates allow Patricia to speed up when her strength is limited?
dawackyman
commented
3 weeks ago Another odd issue I’ve noticed: although my program is set to search at a depth of 15 (and I can change the depth at any time), Patricia still searches 15 moves deep even when I limit her to 500 Elo. From what you mentioned, it sounds like she’s spending the normal amount of time to search but then intentionally picking a suboptimal move. Could you clarify why she’s still going so deep when playing at such a low strength level?
Adam-Kulju
commented
3 weeks ago She slows down and searches as deeply as she can because in order to weaken an engine like Patricia to below 2000, searching for less time is not enough - you have to intentionally play suboptimal moves. So, when Patricia's strength is limited she searches the 5 best moves (which takes 5 times as long as a regular search), then once she is finished searching she uses the information about those 5 moves, together with the ELO she is set to, to determine which to play.
So, if you search her from a random position, she'll come up with this: Move 1 - 1.24 Move 2 - 1.04 Move 3 - 1.0 Move 4 - 0.8 Move 5 - 0.3
and then if her strength is set to 3000 she'll probably play Move 1, while if her strength is set to 500 she'll probably play Move 5.
dawackyman
commented
3 weeks ago She slows down and searches as deeply as she can because in order to weaken an engine like Patricia to below 2000, searching for less time is not enough - you have to intentionally play suboptimal moves. So, when Patricia's strength is limited she searches the 5 best moves (which takes 5 times as long as a regular search), then once she is finished searching she uses the information about those 5 moves, together with the ELO she is set to, to determine which to play.
So, if you search her from a random position, she'll come up with this: Move 1 - 1.24 Move 2 - 1.04 Move 3 - 1.0 Move 4 - 0.8 Move 5 - 0.3
and then if her strength is set to 3000 she'll probably play Move 1, while if her strength is set to 500 she'll probably play Move 5.
Thank you for the detailed explanation! It makes sense that in order to weaken an engine like Patricia below 2000, she needs to search deeply and intentionally play suboptimal moves. I appreciate knowing that when her strength is limited, she evaluates the five best moves, which indeed takes longer, and then selects one based on the Elo setting.
However, I’m curious about how this approach impacts the overall gameplay experience, particularly at lower strength levels. For instance, if I set her to 500 Elo but she still searches at a depth of 15, it feels like the gameplay may not align with the expected strength level. This discrepancy could be confusing for users seeking a more consistent experience at these lower settings.
To enhance user experience, is there a specific depth I should consider setting for each rank from 1 to 20? You mentioned that she evaluates five moves, but what would be the optimal depth for different Elo ratings? Finding a balance between search depth and playability would be incredibly beneficial, especially for those of us looking for a more accurately matched challenge at various skill levels in our chess games.
Adam-Kulju
commented
3 weeks ago Patricia with skill levels turned on is mostly calibrated to time; her 3000 setting plays at a 3000 level at 1"+1' time control, for example. That being said, if you're using a low skill level, say 1000, it makes no difference if the depth is 5 or 15, the quality of play will be the same.
dawackyman
commented
3 weeks ago Patricia with skill levels turned on is mostly calibrated to time; her 3000 setting plays at a 3000 level at 1"+1' time control, for example. That being said, if you're using a low skill level, say 1000, it makes no difference if the depth is 5 or 15, the quality of play will be the same.
Thank you for the explanation. It helps a lot. Now one last question. Do I have to change both the Skill_Level and UCI_Elo or just one? Like do I keep the Skill_Level default at 20 and change my UCI_Elo to 800 for example?
Adam-Kulju
commented
3 weeks ago You only need to turn on one. Whichever one is set latest will be the one used.
dawackyman
commented
3 weeks ago Thank you.
I enable UCI_LimitStrength and adjust either UCI_Elo or Skill_Level to set the engine’s Elo to my desired level. However, no matter what I set, the engine takes an absurdly long time to search for a move, which I assume is because my program is overwriting the depth (normally 15) instead of using the limit I set in Patricia's engine settings to 1. This has to be a bug with her v3.1 version, right?
It shouldn't be searching for this long. Usually, other chess engines control the depth dynamically; however, Patricia doesn't seem to do so. My program is overwriting instructions that Patricia is providing (e.g., depth settings). Normally, my program searches at a depth of 15, but when I limit the strength to 1 (500 Elo) in Patricia's engine settings, it's still searching at a depth of 15.
Log below.
Patricia Chess Engine, written by Adam Kulju
id name Patricia 3.1 id author Adam Kulju option name Hash type spin default 32 min 1 max 131072 option name Threads type spin default 1 min 1 max 1024 option name MultiPV type spin default 1 min 1 max 255 option name UCI_LimitStrength type check default false option name Skill_Level type spin default 20 min 1 max 20 option name UCI_Elo type spin default 3000 min 500 max 3000 uciok readyok id name Patricia 3.1 id author Adam Kulju option name Hash type spin default 32 min 1 max 131072 option name Threads type spin default 1 min 1 max 1024 option name MultiPV type spin default 1 min 1 max 255 option name UCI_LimitStrength type check default false option name Skill_Level type spin default 20 min 1 max 20 option name UCI_Elo type spin default 3000 min 500 max 3000 uciok info multipv 1 depth 1 seldepth 1 score cp -10 nodes 32 nps 32000 time 1 pv d1d2 info multipv 2 depth 1 seldepth 3 score cp -20 nodes 65 nps 65000 time 1 pv d1c1 info multipv 3 depth 1 seldepth 2 score cp -22 nodes 99 nps 99000 time 1 pv h2h3 info multipv 4 depth 1 seldepth 2 score cp -28 nodes 131 nps 131000 time 1 pv a2a4 info multipv 5 depth 1 seldepth 2 score cp -29 nodes 161 nps 161000 time 1 pv d1f3 info multipv 1 depth 2 seldepth 3 score cp 24 nodes 270 nps 270000 time 1 pv d1d2 d8d6 info multipv 2 depth 2 seldepth 2 score cp 15 nodes 399 nps 399000 time 1 pv d1c1 b7b6 info multipv 3 depth 2 seldepth 2 score cp 14 nodes 561 nps 561000 time 1 pv h2h3 d8d6 info multipv 4 depth 2 seldepth 2 score cp 12 nodes 690 nps 690000 time 1 pv a2a4 d8d6 info multipv 5 depth 2 seldepth 6 score cp 7 nodes 883 nps 441500 time 2 pv d3b5 c8d7 info multipv 1 depth 3 seldepth 5 score cp 20 nodes 1104 nps 552000 time 2 pv d1d2 d8d6 h6f4 info multipv 2 depth 3 seldepth 9 score cp 2 nodes 1714 nps 857000 time 2 pv d1c1 b7b6 d3b5 info multipv 3 depth 3 seldepth 3 score cp 2 nodes 1924 nps 641333 time 3 pv d1f3 d8d6 h6f4 info multipv 4 depth 3 seldepth 3 score cp -8 nodes 2160 nps 720000 time 3 pv d1c2 b7b6 d3b5 info multipv 5 depth 3 seldepth 3 score cp -10 nodes 2428 nps 809333 time 3 pv h2h3 d8d6 d1d2 info multipv 1 depth 4 seldepth 4 score cp 22 nodes 2564 nps 854666 time 3 pv h2h3 d8d6 d1d2 b7b6 info multipv 2 depth 4 seldepth 6 score cp 22 nodes 2775 nps 925000 time 3 pv d1d2 d8d6 h2h3 b7b6 info multipv 3 depth 4 seldepth 7 score cp 11 nodes 3150 nps 787500 time 4 pv d1c1 d8d6 h6f4 e7e5 info multipv 4 depth 4 seldepth 5 score cp -8 nodes 3346 nps 836500 time 4 pv d1f3 d8d6 h6f4 e7e5 info multipv 5 depth 4 seldepth 6 score cp 5 nodes 3728 nps 932000 time 4 pv g1h1 b7b6 f2f4 e7e6 info multipv 1 depth 5 seldepth 9 score cp 20 nodes 4406 nps 881200 time 5 pv d1d2 d8d6 h2h3 b7b6 h6f4 info multipv 2 depth 5 seldepth 7 score cp 20 nodes 4989 nps 997800 time 5 pv h2h3 d8d6 d1d2 b7b6 h6f4 info multipv 3 depth 5 seldepth 15 score cp 10 nodes 5660 nps 943333 time 6 pv d1f3 d8d6 h2h3 b7b6 d3b5 info multipv 4 depth 5 seldepth 5 score cp 16 nodes 6118 nps 1019666 time 6 pv a2a4 d8d6 d1d2 b7b6 h6f4 info multipv 5 depth 5 seldepth 7 score cp 4 nodes 6959 nps 994142 time 7 pv g1h1 b7b6 d1f3 d8d6 d3b5 info multipv 1 depth 6 seldepth 10 score cp 20 nodes 8152 nps 1164571 time 7 pv d1d2 d8d6 a2a4 b7b6 d3b5 c8d7 info multipv 2 depth 6 seldepth 10 score cp 20 nodes 9743 nps 1082555 time 9 pv a2a4 b7b6 d1d2 d8d6 d3b5 c8d7 info multipv 3 depth 6 seldepth 9 score cp 13 nodes 12483 nps 1248300 time 10 pv h2h3 d8d6 d1d2 b7b6 h6f4 e7e5 info multipv 4 depth 6 seldepth 6 score cp 12 nodes 13007 nps 1182454 time 11 pv d1f3 d8d6 h2h3 a7a6 h6f4 e7e5 info multipv 5 depth 6 seldepth 6 score cp -5 nodes 13686 nps 1244181 time 11 pv g1h1 b7b6 d1f3 a7a6 f3g3 b6b5 info multipv 1 depth 7 seldepth 14 score cp 20 nodes 18078 nps 1291285 time 14 pv g1h1 b7b6 d1d2 a7a6 f2f4 b6b5 f4f5 info multipv 2 depth 7 seldepth 10 score cp 7 upperbound nodes 18770 nps 1251333 time 15 pv g1h1 info multipv 2 depth 7 seldepth 13 score cp 10 nodes 23178 nps 1287666 time 18 pv d1d2 d8d6 a2a4 b7b6 a4a5 c8d7 h6f4 e7e5 info multipv 3 depth 7 seldepth 9 score cp -4 upperbound nodes 23421 nps 1301166 time 18 pv g1h1 info multipv 3 depth 7 seldepth 8 score cp -11 upperbound nodes 23556 nps 1308666 time 18 pv g1h1 info multipv 3 depth 7 seldepth 14 score cp -28 upperbound nodes 23791 nps 1321722 time 18 pv g1h1 info multipv 3 depth 7 seldepth 14 score cp 17 nodes 28248 nps 1284000 time 22 pv d1f3 d8d6 h2h3 b7b6 d3b5 c8d7 h6f4 info multipv 4 depth 7 seldepth 12 score cp 0 upperbound nodes 28626 nps 1301181 time 22 pv g1h1 info multipv 4 depth 7 seldepth 8 score cp -27 upperbound nodes 28737 nps 1306227 time 22 pv g1h1 info multipv 4 depth 7 seldepth 9 score cp -39 upperbound nodes 28851 nps 1311409 time 22 pv g1h1 info multipv 4 depth 7 seldepth 11 score cp 10 nodes 32263 nps 1344291 time 24 pv a2a4 b7b6 d1d2 d8d6 a4a5 c8d7 h6f4 info multipv 5 depth 7 seldepth 7 score cp 0 upperbound nodes 32416 nps 1350666 time 24 pv g1h1 info multipv 5 depth 7 seldepth 8 score cp -27 upperbound nodes 32535 nps 1301400 time 25 pv g1h1 info multipv 5 depth 7 seldepth 8 score cp -39 upperbound nodes 32648 nps 1305920 time 25 pv g1h1 info multipv 5 depth 7 seldepth 16 score cp 0 nodes 38042 nps 1358642 time 28 pv h2h4 d8d6 d1d2 b7b6 d3b5 c8d7 h6f4 info multipv 1 depth 8 seldepth 18 score cp 10 lowerbound nodes 41049 nps 1368300 time 30 pv h2h4 info multipv 1 depth 8 seldepth 15 score cp 21 lowerbound nodes 48860 nps 1357222 time 36 pv d1f3 info multipv 1 depth 8 seldepth 14 score cp 8 nodes 54664 nps 1366600 time 40 pv d1f3 d8d6 h2h4 b7b6 h4h5 c8d7 h5g6 h7g6 h6f4 info multipv 2 depth 8 seldepth 12 score cp -2 upperbound nodes 56741 nps 1383926 time 41 pv d1f3 info multipv 2 depth 8 seldepth 13 score cp -12 upperbound nodes 58818 nps 1367860 time 43 pv d1f3 info multipv 2 depth 8 seldepth 18 score cp -1 nodes 68141 nps 1390632 time 49 pv d1d2 d8d6 a2a4 b7b6 h2h3 c8d7 a4a5 b6a5 info multipv 3 depth 8 seldepth 21 score cp -11 upperbound nodes 70287 nps 1378176 time 51 pv d1f3 info multipv 3 depth 8 seldepth 16 score cp 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score cp -92 upperbound nodes 1135792 nps 1305508 time 870 pv d1d2 info multipv 2 depth 12 seldepth 19 score cp -144 upperbound nodes 1159393 nps 1305622 time 888 pv d1d2 info multipv 2 depth 12 seldepth 25 score cp -9 nodes 1415776 nps 1313335 time 1078 pv d3c4 a8b8 a2a4 e7e6 d1d2 e6d5 h6g5 d8c7 g5f4 d4e5 f4e5 c7e5 info multipv 3 depth 12 seldepth 11 score cp -22 upperbound nodes 1416635 nps 1312914 time 1079 pv d1d2 info multipv 3 depth 12 seldepth 7 score cp -30 upperbound nodes 1416715 nps 1312988 time 1079 pv d1d2 info multipv 3 depth 12 seldepth 7 score cp -58 upperbound nodes 1416798 nps 1313065 time 1079 pv d1d2 info multipv 3 depth 12 seldepth 12 score cp -95 upperbound nodes 1417095 nps 1312125 time 1080 pv d1d2 info multipv 3 depth 12 seldepth 15 score cp -104 upperbound nodes 1417606 nps 1312598 time 1080 pv d1d2 info multipv 3 depth 12 seldepth 17 score cp -154 upperbound nodes 1419309 nps 1312959 time 1081 pv d1d2 info multipv 3 depth 12 seldepth 25 score cp -14 nodes 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