Iriri is a pattern matching game for icehouse pyramids. Players score points by laying words on a grid in a crossword-like manner. The possible words are determined by a dictionary, which shrinks as the game goes on.
Generated at 2023-06-18, 15:21 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!
Iriri is played with 1 stash of icehouse pyramids per player, meaning 15 pyramids, 5 of each size. Alternatively, any 6 sets of 5 tokens can be used, as long as it is clear to which player each belongs.
Each player chooses a (different) colour and takes a stash (i.e. 15 pyramids, 5 of each size) in that colour.
Each player takes 1 pyramid of each size from their stash into their hand.
Shuffle all other pieces together and place them in a circle in random order. This is the dictionary.
Player 1 begins the place phase of his turn by placing a valid word (of at least 2 pieces) in the empty play area. For this first turn on the empty board, player 1 scores three times the number of placed pieces + 0.5. Play continues as described below (i.e. with the take phase of player 1).
A turn consists of two phases: place and take. They are always performed in that order by a single player, after which the turn passes to the next player. Both phases are mandatory (i.e. no passing or skipping a phase), unless no move is possible.
Place one or more pieces from your hand on the board, connected (orthogonally) in at least one place to the rest of the pyramids.
All placed pieces must lie on a single horizontal or vertical line, with no empty spaces between them.
Pieces may be placed on both sides of existing pieces, between existing pieces, etc.. as long as they satisfy the above rule.
Each word on the play area of which the newly placed pieces are part, both horizontal and vertical, must be a valid word at the time of placement, according to the current dictionary.
For each word you formed, score 1 point per piece. Although you may only place pieces of your own colour, you score points for pieces of the opponent’s colour as well. If a piece is part of 2 newly formed words, count it twice.
If you can’t form a valid word, you must pass.
Refill your hand to 3 pieces, by taking pieces of your colour from the dictionary. You cannot take the last piece of a specific type (size + colour), so one piece of every type will remain in the dictionary to the very end. Other than this, you may freely choose which pieces to take, i.e. any combination of sizes from whichever positions you like.
After removing a piece from the dictionary, close the gap so that the pieces on either side are now adjacent.
If too few pieces remain to fill up your hand, take as many as possible.
If you can’t take any more pieces, skip this phase. It does not count as a pass. You may still perform the place phase of your next turn even with fewer than 3 pieces in hand.
The game ends when no player can make a move, i.e. all players have been forced to pass in succession.
The player with the highest score is the winner.
General comments:
Play: Combinatorial
Family: Combinatorial 2022
Mechanism(s): Pattern
Components: Board
Level: Standard
BGG Entry | Iriri |
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BGG Rating | 7 |
#Voters | 5 |
SD | 0 |
BGG Weight | 2 |
#Voters | 1 |
Year | 2022 |
User | Rating | Comment |
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jdonland | 7 | |
Carthoris | 7 | It's sort of a Qwirkle thing, i.e. Scrabble with abstract signs instead of natural language. But it takes another cue from MeM for pattern-matching based on the arbitrary arrangement of the piece reserve. It's an interesting little two-player challenge. |
fiddly_bits | N/A | I have the old-school stashes... |
mrraow | 7 | Certainly an original concept; but has a couple of conceptual hurdles (e.g the need to re-evaluate all legal moves after each removal from the dictionary) that mean I'd prefer to admire it from a distance. |
UanarchyK | 7 | Scrabble with variable dictionary which is edited during play Played solo against self to test out. Seems like scores will often be very similar. Very little opportunity for lookahead. Extremely tactical. Half the game takes place in the dictionary. |
RichardIngram | 7 |
Size (bytes) | 37112 |
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Reference Size | 10673 |
Ratio | 3.48 |
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.
Playouts per second | 17644.03 (56.68µs/playout) |
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Reference Size | 601106.04 (1.66µs/playout) |
Ratio (low is good) | 34.07 |
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.
% new positions/bucket
State Space Complexity | 37321737 | |
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State Space Complexity bounds | 2266849 < 37321737 < ∞ | |
State Space Complexity (log 10) | 7.57 |   |
State Space Complexity bounds (log 10) | 6.36 <= 7.57 <= ∞ | |
Samples | 40463 | |
Confidence | 0.00 | 0: totally unreliable, 100: perfect |
State space complexity (where present) is an estimate of the number of distinct game tree reachable through actual play. Over a series of random games, Ai Ai checks each position to see if it is new, or a repeat of a previous position and keeps a total for each game. As the number of games increase, the quantity of new positions seen per game decreases. These games are then partitioned into a number of buckets, and if certain conditions are met, Ai Ai treats the number in each bucket as the start of a strictly decreasing geometric sequence and sums it to estimate the total state space. The accuracy is calculated as 1-[end bucket count]/[starting bucklet count]
Label | Its/s | SD | Nodes/s | SD | Game length | SD |
---|---|---|---|---|---|---|
Random playout | 69 | 1 | 3,264 | 63 | 48 | 7 |
search.UCT | 18,160 | 276 | 44 | 11 |
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.
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.
1: Blue win % | 33.00±2.84 | Includes draws = 50% |
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2: Yellow win % | 67.00±2.97 | Includes draws = 50% |
Draw % | 0.00 | Percentage of games where all players draw. |
Decisive % | 100.00 | Percentage of games with a single winner. |
Samples | 1000 | Quantity 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.
Match | AI | Strong Wins | Draws | Strong Losses | #Games | Strong Score | p1 Win% | Draw% | p2 Win% | Game Length |
---|---|---|---|---|---|---|---|---|---|---|
0 | Random | |||||||||
2 | UCT (its=3) | 631 | 0 | 233 | 864 | 0.6998 <= 0.7303 <= 0.7588 | 39.70 | 0.00 | 60.30 | 47.69 |
4 | UCT (its=22) | 631 | 0 | 141 | 772 | 0.7886 <= 0.8174 <= 0.8430 | 47.54 | 0.00 | 52.46 | 46.83 |
5 | UCT (its=60) | 631 | 0 | 233 | 864 | 0.6998 <= 0.7303 <= 0.7588 | 38.08 | 0.00 | 61.92 | 45.96 |
6 | UCT (its=164) | 631 | 0 | 264 | 895 | 0.6743 <= 0.7050 <= 0.7340 | 38.55 | 0.00 | 61.45 | 45.21 |
7 | UCT (its=445) | 631 | 0 | 291 | 922 | 0.6537 <= 0.6844 <= 0.7136 | 38.39 | 0.00 | 61.61 | 44.38 |
8 | UCT (its=1210) | 631 | 0 | 359 | 990 | 0.6069 <= 0.6374 <= 0.6667 | 36.57 | 0.00 | 63.43 | 44.55 |
9 | UCT (its=3290) | 460 | 0 | 292 | 752 | 0.5764 <= 0.6117 <= 0.6459 | 31.91 | 0.00 | 68.09 | 43.79 |
10 | UCT (its=3290) | 482 | 0 | 518 | 1000 | 0.4512 <= 0.4820 <= 0.5130 | 32.60 | 0.00 | 67.40 | 43.83 |
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.
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 length | NaN | |
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Branching factor | NaN |   |
Complexity | 10^NaN | Based on game length and branching factor |
Computational Complexity | 10^-∞ | Saturation reached - accuracy very high. |
Samples | 1000 | Quantity 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.
Board Size | 676 | Quantity of distinct board cells |
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Distinct actions | 135 | Quantity of distinct moves (e.g. "e4") regardless of position in game tree |
Killer moves | 2 | A 'killer' move is selected by the AI more than 50% of the time Killers: Hand 2 -> aa26,Hand 0 -> ab25 |
Good moves | 0 | A good move is selected by the AI more than the average |
Bad moves | 134 | A bad move is selected by the AI less than the average |
Terrible moves | 20 | A terrible move is never selected by the AI Too many terrible moves to list. |
Samples | 1000 | Quantity of logged games played |
A mean of NaN% of board locations were used per game.
Colour and size show the frequency of visits.
Game length frequencies.
Mean | 42.46 |
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Mode | [50] |
Median | 10.0 |
Mean change in material/round | 0.17 | Complete round of play (all players) |
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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.)
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 7% of the game turns. Ai Ai found 0 critical turns (turns with only one good option).
This chart shows the relative temperature of all moves each turn. Colour range: black (worst), red, orange(even), yellow, white(best).
Measure | All players | Player 1 | Player 2 |
---|---|---|---|
Mean % of effective moves | 87.96 | 86.25 | 89.13 |
Mean no. of effective moves | 5.79 | 5.59 | 5.92 |
Effective game space | 10^26.06 | 10^9.97 | 10^16.09 |
Mean % of good moves | 62.29 | 10.91 | 97.22 |
Mean no. of good moves | 4.52 | 0.94 | 6.96 |
Good move game space | 10^19.59 | 10^2.08 | 10^17.51 |
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.
Measure | Value | Description |
---|---|---|
Hot turns | 47.62% | A hot turn is one where making a move is better than doing nothing. |
Momentum | 26.19% | % of turns where a player improved their score. |
Correction | 19.05% | % of turns where the score headed back towards equality. |
Depth | 2.71% | Difference in evaluation between a short and long search. |
Drama | 0.00% | How much the winner was behind before their final victory. |
Foulup Factor | 40.48% | Moves that looked better than the best move after a short search. |
Surprising turns | 2.38% | Turns that looked bad after a short search, but good after a long one. |
Last lead change | 11.90% | Distance through game when the lead changed for the last time. |
Decisiveness | 90.48% | 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.)
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
1 | 3 | 63 | 727 | 8267 | 101495 | 976219 | 12215759 |
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.
No solutions found to depth 7.
Puzzle | Solution |
---|---|
Blue to win in 12 moves | |
Blue to win in 10 moves | |
Blue to win in 13 moves | |
Blue to win in 9 moves | |
Blue to win in 12 moves | |
Yellow to win in 8 moves | |
Blue to win in 16 moves | |
Blue to win in 14 moves | |
Yellow to win in 8 moves | |
Blue to win in 5 moves | |
Blue to win in 6 moves | |
Yellow to win in 4 moves |
Weak puzzle selection criteria are in place; the first move may not be unique.