When SilverJacket wrote an artificially intelligent program to play the game Codenames, he was surprised by its performance—at once tremendously intelligent and annoyingly literal
, and of course chess. I decided to try my hand at making a program* that performs passably at Codenames.
I knew of a shortcut. It’s called word embeddings. Computer scientists have developed a method to encode the meanings of words in numbers. An algorithm trawls through billions of words of text on the internet and looks at how often each vocabulary word appears in the vicinity of every other vocabulary word. Then it assigns each a sequence of perhaps 300 numbers summarizing its relationship to other words. This sequence represents its location in a 300-dimensional space of meaning.
Here’s how my program generates clues: It first tries to find a clue that will target every single one of its team’s words that remain on the board. At the beginning of a game, that’s nine, a herculean task. It scans through the 50,000 possible clues and looks for words that are closer to all nine of those words than to any of the opponents’ words or to the bomb word.
Of course, the software has weaknesses not captured in that study. I would never attempt “sin, 3” with this program as a guesser, because “fall” makes sense only through its association with “angel,” and the program looks only at single-hop similarities between clues and cards. Indeed, when I entered that clue, it correctly guessed “thief” and “angel” but incorrectly guessed “charge,” ending the game. It misses obvious clues in head-smacking ways.
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