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Parlante, Nick; Zelenski, Julie; Schwarz, Keith; Feinberg, Dave; Craig, Michelle; Hansen, Stuart; Scott, Michael; Malan, David J.

doi:10.1145/1953163.1953305

Cited by 4 publications

(2 citation statements)

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“…We consider the problem in the context of a perfect guesser, with perfect knowledge of the language. There is a little twist to the game that has been widely used as a programming assignment [11]. In the Evil Hangman variant, the setter can change the secret word as often as she wants during the game, as long as at the end she's able to reveal a word that is consistent with all the answers given thus far to the guesser's queries.…”

Section: Introductionmentioning

confidence: 99%

“…In the Evil Hangman variant, the setter can change the secret word as often as she wants during the game, as long as at the end she's able to reveal a word that is consistent with all the answers given thus far to the guesser's queries. In the programming assignment [11], students are given the task of implementing what we call the GreedyCheater, an evil setter that decides to answer each query with the heuristic of keeping the dictionary of consistent words to reveal at the end as big as possible.…”

Section: Introductionmentioning

confidence: 99%

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The Computational Complexity of Evil Hangman

Barbay,

Subercaseaux

2020

Preprint

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The game of Hangman is a classical asymmetric two player game in which one player, the setter, chooses a secret word from a language, that the other player, the guesser, tries to discover through single letter matching queries, answered by all occurrences of this letter if any. In the Evil Hangman variant, the setter can change the secret word during the game, as long as the new choice is consistent with the information already given to the guesser. We show that a greedy strategy for Evil Hangman can perform arbitrarily far from optimal, and most importantly, that playing optimally as an Evil Hangman setter is computationally difficult. The latter result holds even assuming perfect knowledge of the language, for several classes of languages, ranging from Finite to Turing Computable. The proofs are based on reductions to Dominating Set on 3-regular graphs and to the Membership problem, combinatorial problems already known to be computationally hard.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%