wchargin

Joined 12 years ago

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Comment · 2 years ago
Hmm… consider the following. Your FSM is acyclic iff you can assign each state an integer depth such that a state at depth d only ever transitions to states at depths strictly greater than d. So consider the following tables:

    CREATE TABLE states (
        state order_state PRIMARY KEY,
        depth int NOT NULL,
        UNIQUE(state, depth)
    );
    CREATE TABLE transitions (
        start_state order_state NOT NULL,
        event order_event NOT NULL,
        end_state order_state NOT NULL,
        start_depth int NOT NULL,
        end_depth int NOT NULL,
        CONSTRAINT transitions_start_depth_correct
            FOREIGN KEY(start_state, start_depth) REFERENCES states(state, depth)
            ON UPDATE CASCADE,
        CONSTRAINT transitions_end_depth_correct
            FOREIGN KEY(end_state, end_depth) REFERENCES states(state, depth)
            ON UPDATE CASCADE,
        CONSTRAINT transitions_depth_increases CHECK(end_depth > start_depth),
        PRIMARY KEY(start_state, event)
    );
Let's bang on it for a quick test. You can define a state machine; here's one that roughly matches the regex `^(AB|BA)$` (I know I'm being a bit sloppy):

    fsm=> CREATE DOMAIN order_state AS text;
    CREATE DOMAIN
    fsm=> CREATE DOMAIN order_event AS text;
    CREATE DOMAIN
    fsm=> -- "CREATE TABLE"s as above, then:
    fsm=> INSERT INTO states(state, depth) VALUES('start', 1), ('a', 2), ('b', 2), ('done', 3), ('error', 3);
    INSERT 0 5
    fsm=> INSERT INTO transitions(start_state, event, end_state, start_depth, end_depth) VALUES('start', 'A', 'a', 1, 2), ('start', 'B', 'b', 1, 2), ('a', 'B', 'done', 2, 3), ('b', 'A', 'done', 2, 3), ('a', 'A', 'error', 2, 3), ('b', 'B', 'error', 2, 3);
    INSERT 0 6
And, as you need to modify it, you can increase a node's depth to make room for intervening nodes:

    fsm=> UPDATE states SET depth = 4 WHERE state = 'error';
    UPDATE 1
    fsm=> TABLE transitions;
     start_state | event | end_state | start_depth | end_depth 
    -------------+-------+-----------+-------------+-----------
     start       | A     | a         |           1 |         2
     start       | B     | b         |           1 |         2
     a           | B     | done      |           2 |         3
     b           | A     | done      |           2 |         3
     a           | A     | error     |           2 |         4
     b           | B     | error     |           2 |         4
    (6 rows)
But you can't decrease a node's depth too far:

    fsm=> UPDATE states SET depth = 2 WHERE state = 'error';
    ERROR:  new row for relation "transitions" violates check constraint "transitions_depth_increases"
    DETAIL:  Failing row contains (a, A, error, 2, 2).
    CONTEXT:  SQL statement "UPDATE ONLY "public"."transitions" SET "end_state" = $1, "end_depth" = $2 WHERE $3::pg_catalog.text OPERATOR(pg_catalog.=) "end_state"::pg_catalog.text AND $4 OPERATOR(pg_catalog.=) "end_depth""
And you can't introduce transitions that don't increase depth:

    fsm=> INSERT INTO transitions(start_state, event, end_state, start_depth, end_depth) VALUES('done', 'AGAIN!', 'start', 3, 1);
    ERROR:  new row for relation "transitions" violates check constraint "transitions_depth_increases"
    DETAIL:  Failing row contains (done, AGAIN!, start, 3, 1).
Now, I don't know that I would immediately recommend this for high-throughput production use. You're storing "unnecessary" state not once but many times (each state's depth appears `1 + \deg(v)` times), plus additional indices and lookups. But I do think it meets the desired consistency goals!
Comment · 3 years ago
Comment · 6 years ago
Hey, I wrote one of these, too! Mine generates and solves all possible puzzles (with my dictionary, there are 54733) in about 1.8 seconds on my six-year-old laptop, and can also typeset them to LaTeX+TikZ to make printable puzzle sheets. That’s 0.03 milliseconds per puzzle. It turns out that you can get this thing to run quite fast once you realize that you can pack words into bitsets and express the whole algorithm in terms of bitwise operations on machine words. Of particular interest is that the running time is output-sensitive: proportional to the number of solutions to the puzzle but not the total size of the dictionary (after one-time preprocessing).

I used the “hard copy” rule set as used in the printed New York Times, which is slightly different from the web rule set: words must be at least five letters long, not four, and are worth 1 point each, or 3 for a pangram. The hard copy puzzles also include three score thresholds, so I had a bit of fun trying to reverse-engineer how those thresholds are chosen. I didn’t get exactly the right function, but I got fairly close, and most importantly the thresholds feel fair when playing. (The online version also has score thresholds, but there are many more of them, and it was easy for me to transcribe thresholds from the archives of the printed copies.)

In my admittedly biased yet assuredly humble opinion, both the algorithm performance and the rating threshold estimation are interesting: https://github.com/wchargin/spelling-bee/tree/master#perform...

Oh, and a web-based solver, for convenience: https://wchargin.github.io/spelling-bee/

Lovely to see how the author of this article and I both had a lot of fun with this by taking it in different directions. :-)

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