Abstract
The skills required to solve crossword puzzles involve two important aspects of lexical memory: semantic information in the form of clues that indicate the meaning of the answer, and orthographic patterns that constrain the possibilities but may also provide hints to possible answers. Mueller and Thanasuan (2013) proposed a model accounting for the simple memory access processes involved in solving individual crossword clues, but expert solvers also bring additional skills and strategies to bear on solving complete puzzles. In this paper, we developed an computational model of crossword solving that incorporates strategic and other factors, and is capable of solving crossword puzzles in a human-like fashion, in order to understand the complete set of skills needed to solve a crossword puzzle. We compare our models to human expert and novice solvers to investigate how different strategic and structural factors in crossword play impact overall performance. Results reveal that expert crossword solving relies heavily on fluent semantic memory search and retrieval, which appear to allow experts to take better advantage of orthographic-route solutions, and experts employ strategies that enable them to use orthographic information. Furthermore, other processes central to traditional AI models (error correction and backtracking) appear to be of less importance for human players.
Highlights
Crossword puzzles were first introduced in 1913, and have become both a popular pastime, mental training aid, and a domain of study for psychological researchers (e.g., Nickerson, 2011), who have long acknowledged the role of memory access in puzzle solving
Mueller and Thanasuan (2014) we proposed a model of the basic memory search processes involved in solving individual crossword clues, and suggest that the joint access and constraint provided by cues in crossword puzzles make it similar to expert decision making in many domains
For many of the same reasons that make them engaging puzzles for humans, crossword puzzles pose an interesting problem for Artificial Intelligence (AI) systems, as solving them requires using many of the fundamental aspects of modern AI: search, heuristics, constraint satisfaction, knowledge representation, optimization, and data mining
Summary
Crossword puzzles were first introduced in 1913, and have become both a popular pastime, mental training aid, and a domain of study for psychological researchers (e.g., Nickerson, 2011), who have long acknowledged the role of memory access in puzzle solving. Mueller and Thanasuan (2014) we proposed a model of the basic memory search processes involved in solving individual crossword clues, and suggest that the joint access and constraint provided by cues in crossword puzzles make it similar to expert decision making in many domains. For many of the same reasons that make them engaging puzzles for humans, crossword puzzles pose an interesting problem for Artificial Intelligence (AI) systems, as solving them requires using many of the fundamental aspects of modern AI: search, heuristics, constraint satisfaction, knowledge representation, optimization, and data mining. “Dr Fill” (Ginsberg, 2011) is currently the best-known and most advanced AI crossword solver, and it typically performs perfectly on most “straight” puzzles, only making mistakes on puzzles with complex or unusual themes or letter arrangements (Lohr, 2012). The improvement over time is related to broader knowledge corpora being used, and the incorporation of more rules for handling tricky puzzle themes, which often include puns, rebuses (i.e., letter substitutions), and other wordplay devices
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