Full Report for Resolve by Alek Erickson

Full Report for Resolve by Alek Erickson

Resolve is a connection game for two players

Generated at 10/03/2021, 03:10 from 1000 logged games.


Representative game (in the sense of being of mean length). Wherever you see the 'representative game' referred to in later sections, this is it!

Same-colored stones with orthogonal adjacency are connected.

The game is over when a player wins by connecting their designated sides of the board with a single group of connected stones of their color, at any time during their turn or their opponent's turn. Cutting stones are four stones in the following generic crosscut configuration:


On your turn you may select one of two actions:

  1. Place a stone of your color on an empty point. If that stone creates a crosscut, swap it with different adjacent enemy stones that share a crosscut with it, until that stone is no longer part of a crosscut.
  2. Choose a stone of your color that is part of a crosscut, and use it to resolve crosscuts as in 1) . Then place a stone of your color on an empty point, if possible.

Resolve was designed by Alek Erickson in July 2020, and the rules were eventually finalized through critical discussions and play testing with Dale Walton and Luis Bolaños Mures. The game was partly inspired by Michal Zapawa's swap mechanic from Slyde. The original idea for swapping stones to resolve crosscuts can be traced to Phil Leduc's Thruway and Bill Taylor's Swapway as early as 2008, but the "resolving stone" mechanism, where a single stone gets serially swapped to fix cuts in Resolve is novel.


General comments:

Play: Combinatorial

Family: Combinatorial 2020

Mechanism(s): Connection

Level: Standard

BGG Stats

BGG EntryResolve
BGG Ratingnull
BGG Weightnull

Kolomogorov Complexity Analysis

Size (bytes)27578
Reference Size10293

Ai Ai calculates the size of the implementation, and compares it to the Ai Ai implementation of the simplest possible game (which just fills the board). Note that this estimate may include some graphics and heuristics code as well as the game logic. See the wikipedia entry for more details.

Playout Complexity Estimate

Playouts per second3049.48 (327.92µs/playout)
Reference Size421407.50 (2.37µs/playout)
Ratio (low is good)138.19

Tavener complexity: the heat generated by playing every possible instance of a game with a perfectly efficient programme. Since this is not possible to calculate, Ai Ai calculates the number of random playouts per second and compares it to the fastest non-trivial Ai Ai game (Connect 4). This ratio gives a practical indication of how complex the game is. Combine this with the computational state space, and you can get an idea of how strong the default (MCTS-based) AI will be.

Playout/Search Speed

LabelIts/sSDNodes/sSDGame lengthSD
Random playout3,554185615,82331,96217313

Random: 10 second warmup for the hotspot compiler. 100 trials of 1000ms each.

Other: 100 playouts, means calculated over the first 5 moves only to avoid distortion due to speedup at end of game.

Mirroring Strategies

Rotation (Half turn) lost each game as expected.
Reflection (X axis) lost each game as expected.
Reflection (Y axis) lost each game as expected.
Copy last move lost each game as expected.

Mirroring strategies attempt to copy the previous move. On first move, they will attempt to play in the centre. If neither of these are possible, they will pick a random move. Each entry represents a different form of copying; direct copy, reflection in either the X or Y axis, half-turn rotation.

Win % By Player (Bias)

1: White win %53.50±3.10Includes draws = 50%
2: Black win %46.50±3.07Includes draws = 50%
Draw %0.00Percentage of games where all players draw.
Decisive %100.00Percentage of games with a single winner.
Samples1000Quantity of logged games played

Note: that win/loss statistics may vary depending on thinking time (horizon effect, etc.), bad heuristics, bugs, and other factors, so should be taken with a pinch of salt. (Given perfect play, any game of pure skill will always end in the same result.)

Note: Ai Ai differentiates between states where all players draw or win or lose; this is mostly to support cooperative games.

UCT Skill Chains

MatchAIStrong WinsDrawsStrong Losses#GamesStrong Scorep1 Win%Draw%p2 Win%Game Length
1UCT (its=2)63103509810.6127 <= 0.6432 <= 0.672654.230.0045.77170.60
4UCT (its=5)63103659960.6031 <= 0.6335 <= 0.662949.300.0050.70169.41
14UCT (its=15)63103479780.6147 <= 0.6452 <= 0.674647.440.0052.56170.30
UCT (its=18)
0.4980 <= 0.5290 <= 0.5598
UCT (its=18)
0.4870 <= 0.5180 <= 0.5488

Search for levels ended: time limit reached.

Level of Play: Strong beats Weak 60% of the time (lower bound with 95% confidence).

Draw%, p1 win% and game length may give some indication of trends as AI strength increases.

1st Player Win Ratios by Playing Strength

This chart shows the win(green)/draw(black)/loss(red) percentages, as UCT play strength increases. Note that for most games, the top playing strength show here will be distinctly below human standard.


Game length101.96 
Branching factor112.49 
Complexity10^193.94Based on game length and branching factor
Computational Complexity10^7.56Sample quality (100 best): 20.84
Samples1000Quantity of logged games played

Computational complexity (where present) is an estimate of the game tree reachable through actual play. For each game in turn, Ai Ai marks the positions reached in a hashtable, then counts the number of new moves added to the table. Once all moves are applied, it treats this sequence as a geometric progression and calculates the sum as n-> infinity.

Move Classification

Distinct actions793Number of distinct moves (e.g. "e4") regardless of position in game tree
Killer moves120A 'killer' move is selected by the AI more than 50% of the time
Too many killers to list.
Good moves697A good move is selected by the AI more than the average
Bad moves96A bad move is selected by the AI less than the average
Response distance4.85Mean distance between move and response; a low value relative to the board size may mean a game is tactical rather than strategic.
Samples1000Quantity of logged games played

Board Coverage

A mean of 54.48% of board locations were used per game.

Colour and size show the frequency of visits.

Game Length

Game length frequencies.


Change in Material Per Turn

This chart is based on a single representative* playout, and gives a feel for the change in material over the course of a game. (* Representative in the sense that it is close to the mean length.)


Table: branching factor per turn, based on a single representative* game. (* Representative in the sense that it is close to the mean game length.)

Action Types per Turn

This chart is based on a single representative* game, and gives a feel for the types of moves available throughout that game. (* Representative in the sense that it is close to the mean game length.)

Red: removal, Black: move, Blue: Add, Grey: pass, Purple: swap sides, Brown: other.


This chart shows the best move value with respect to the active player; the orange line represents the value of doing nothing (null move).

The lead changed on 60% of the game turns. Ai Ai found 10 critical turns (turns with only one good option).

Position Heatmap

This chart shows the relative temperature of all moves each turn. Colour range: black (worst), red, orange(even), yellow, white(best).

Good/Effective moves

MeasureAll playersPlayer 1Player 2
Mean % of effective moves3.742.315.12
Mean no. of effective moves1.731.701.75
Effective game space10^6.8810^3.2210^3.66
Mean % of good moves10.8616.315.62
Mean no. of good moves8.3412.784.08
Good move game space10^24.9210^16.5110^8.41

These figures were calculated over a single game.

An effective move is one with score 0.1 of the best move (including the best move). -1 (loss) <= score <= 1 (win)

A good move has a score > 0. Note that when there are no good moves, an multiplier of 1 is used for the game space calculation.

Quality Measures

Hot turns96.08%A hot turn is one where making a move is better than doing nothing.
Momentum37.25%% of turns where a player improved their score.
Correction48.04%% of turns where the score headed back towards equality.
Depth15.01%Difference in evaluation between a short and long search.
Drama1.52%How much the winner was behind before their final victory.
Foulup Factor12.75%Moves that looked better than the best move after a short search.
Surprising turns0.00%Turns that looked bad after a short search, but good after a long one.
Last lead change78.43%Distance through game when the lead changed for the last time.
Decisiveness2.94%Distance from the result being known to the end of the game.

These figures were calculated over a single representative* game, and based on the measures of quality described in "Automatic Generation and Evaluation of Recombination Games" (Cameron Browne, 2007). (* Representative, in the sense that it is close to the mean game length.)

Opening Heatmap

Colour shows the success ratio of this play over the first 10moves; black < red < yellow < white.

Size shows the frequency this move is played.

Unique Positions Reachable at Depth


Note: most games do not take board rotation and reflection into consideration.
Multi-part turns could be treated as the same or different depth depending on the implementation.
Counts to depth N include all moves reachable at lower depths.
Inaccuracies may also exist due to hash collisions, but Ai Ai uses 64-bit hashes so these will be a very small fraction of a percentage point.

Shortest Game(s)

No solutions found to depth 3.



Black to win in 4 moves

White to win in 5 moves

White to win in 3 moves

White to win in 4 moves

Black to win in 4 moves

Black to win in 3 moves

White to win in 4 moves

White to win in 4 moves

Black to win in 2 moves

Black to win in 2 moves

Black to win in 2 moves

White to win in 2 moves

White to win in 2 moves

White to win in 2 moves

White to win in 2 moves

Weak puzzle selection criteria are in place; the first move may not be unique.