Below is the e-mail I sent to the cube-lovers mailing list describing my searching algorithm.

From:michael reid <reid@math.brown.edu> [ it's an old e-mail address ]To:cube-lovers@ai.mit.eduSubject:optimal cube solverDate:Sat, 5 Jul 1997 16:58:20 -0400

the recent work by rich korf on finding optimal solutions has prompted me to try my hand at writing an optimal cube solving program. so far, i've done this for the face turn metric. a description of my program follows. let H denote the intermediate subgroup <U, D, F2, R2, B2, L2> which we've seen before. we'll use distances to this intermediate subgroup for our pruning tables (or "pattern databases"). calculating these distances involves doing a breadth first search on the coset space H \ G , and storing these distances in memory. (i've written this as a right coset space, rather than a left coset space.) this search has been done several times, by dik winter and by myself. some review. positions in H are characterized by the following. corners cannot change orientation; their U or D facelet remains on the U or D face. similar, edges cannot change orientation. furthermore, the four U-D slice edges remain in the U-D slice. therefore, cosets in H \ G are described by triples (c, e, l), where c denotes corner orientation, e denotes edge orientation and l denotes the location of the four U-D slice edges. there are 3^7 = 2187 possible corner orientations, 2^11 = 2048 possible edge orientations / 12 \ and \ 4 / = 495 possible U-D slice edge locations. all combinations are possible, so there are 2187 * 2048 * 495 = 2217093120 cosets. since this is too many configurations to store in memory, we use symmetry to to reduce this number. there are 16 symmetries of the cube that preserve the U-D axis, and therefore the intermediate subgroup H. rather than store all the cosets, we'll just store one of each up to symmetry. actually, this is slightly more complicated than necessary; instead, we could just divide the corner coordinate by symmetry. this is what i did in my message of january 7, 1995. however, i encountered a pitfall along the way. i discovered (very late in the development stage) the need for very large transformation tables. although i continued with the same approach at that time, i gave two options for overcoming this problem: > i) only use the 8 symmetries that preserve my choice of > 12 edge facelets. > > ii) combine the two coordinates edge and location into a single > coordinate and divide this coordinate by the 16 symmetries. of these, clearly the second is the better choice, since it utilizes more symmetry. this new edge coordinate has 2048 * 495 = 1013760 possibilities. up to symmetry, there are 64430 possibilities. we need room for 64430 * 2187 = 140908410 cosets in memory. for each of these, we store its distance to the identity coset. this is an integer between 0 and 12 (inclusive), so each is stored in half a byte. thus the whole table requires 67 megabytes. essentially, what we're doing here is changing coordinates from (c, e, l) to (c, e', s), where e' is our new edge coordinate, and s is a symmetry coordinate. some cosets have multiple coordinates in this new system, but that causes no harm. a breadth first search of this space takes under 11 minutes. the increase in speed is partially due to a more powerful computer, and partially due to switching to "backward searching" (or "bidirectional search") at the optimal time. we'll also use distances to the intermediate subgroups <F, B, U2, R2, D2, L2> and <R, L, F2, U2, B2, D2>. we don't need to store additional coset spaces, since we can derive that information from our first coset space. note that the cube rotation C_UFR takes the subgroup <U, D, F2, R2, B2, L2> to the subgroup <F, B, U2, R2, D2, L2>. therefore it transforms the first coset space into the second coset space. furthermore, it preserves distances, so the one pattern database suffices for all three applications. an attractive feature of this approach is that it uses the 16 symmetries to reduce the size of the pattern database, and then uses the remaining symmetry of the cube in applying it in different orientations. these are the only pruning tables my program currently uses. note that they cannot "see the entire group". specifically, let H_0 = <U, D, F2, R2, B2, L2>, H_1 = <F, B, U2, R2, D2, L2>, H_2 = <R, L, F2, U2, B2, D2>, and let T denote the intersection of these three subgroups. for a given position, the three distances to these subgroups depend only upon the corresponding coset in T \ G . thus T might be thought of as a "target subgroup". this target subgroup T is interesting. it consists of those positions that "look like" they're in the "square group" <F2, L2, U2, B2, R2, D2>, i.e. F and B colors mix only with each other, and similarly for R and L , and also for U and D. however, this is strictly larger than the square group; it contains the square group as a subgroup of index 6. the searching is done in the way that korf describes as "IDA*" (or at least the "ID" part of that terminology). we traverse the tree of all sequences of length 1, hoping to find a solution. that generally fails, so we continue to sequences of length 2, and so forth, until a solution is found. the "A*" part of the algorithm is to use the pruning tables to avoid searching large parts of the tree that are guaranteed not to bear fruit. in his paper, korf uses the expected value of his heuristic functions to get an estimate of how effective they are at pruning the search tree. actually, he should subtract 1 from this expected value, since we must generate (at least partially) the top node of a subtree that gets pruned. this is only a rough estimate; getting a more precise figure is a delicate matter which i won't address here. korf reports an expected value of 8.878. i generated 10 million random cubes (i did not use the long sequence of random twists method) and got an expected value of 9.941. my program generates slightly more than 500000 nodes per second. korf generates them at 700000 per second, so i've got more overhead per node. however, it generates many fewer nodes, since it prunes the search tree more efficiently. i solved korf's ten random cubes, and found all minimal solutions, rather than stopping at the first. this entailed one complete search through length 16f, three through length 17f and six through length 18f. the position at distance 16f has a unique minimal solution, as do the three positions at distance 17f. of the six positions at distance 18f, one has a unique minimal solution, one has 3 minimal solutions, two have 4 minimal solutions and two have 6 minimal solutions. the total run time for these was just under 198 hours. korf estimates 4000 hours for the same search, so on these positions, my program is twenty times as fast. my computer has a 200 MHz pentium pro processor, and is configured with 128 megabytes of RAM. i'd expect a similar increase in performance for most positions, but not all. for example, positions inside the target subgroup T run very slowly, as do positions very close to it. hopefully, most of these are close enough to start, so that searches don't have to go very deep. i suspect that there are probably also positions that give korf's program difficulty. as you can see, i've made only minor modifications to korf's method. the only differences are: 1. use different pattern databases that allow more efficient pruning. 2. apply the same pattern database in multiple orientations. 3. allow a target subgroup larger than just the identity. it's clear that more experimentation is needed with different pattern databases. for any subgroup K of G , we could consider distances to that subgroup. it seems likely that we want small subgroups, so that the average distance is large. for this reason, using symmetry to reduce the size of the database is an important tool. i encourage others to experiment with different subgroups. more results to come ... mike

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