In the post Sketching Semantle Solvers, I introduced two methods for solving Semantle word puzzles, but I only wrote up one. The second solver here is based the idea that the target word should appear in the intersection between the cohorts of possible targets generated by each guess.
To recap, the first post:
- introduced the sibling strategies side-by-side,
- discussed designing for sympathetic sequences, so the solver can play along with humans, with somewhat explainable guesses, and
- shared the source code and visualisations for the gradient descent solver.
The solver has the following major features:
- A vocabulary, containing all the words that can be guessed,
- A semantic model, from which the agent can calculate the similarity of word pairs,
- The ability to generate cohorts of words from the vocabulary that are similar (in Semantle score) to a provided word (a guess), and
- An evolving strength of belief that each word in the vocabulary is the target.
In each step towards guessing the target, the solver does the following:
- Choose a word for the guess. The current choice is the word with the strongest likelihood of being the target, but it could equally be any other word from the solver’s vocabulary (which might help triangulate better), or it could be provided by a human player with their own suspicions.
- Score the guess. The Semantle simulator scores the guess.
- Generate a cohort. The guess and the score are used to generate a new cohort of words that would share the same score with the guess.
- Merge the cohort into the agent’s belief model. The score is added to the current belief strength for each word in the cohort, providing a proxy for likelihood for each word. The guess is also masked from further consideration.
Show of strength
The chart below shows how the belief strength (estimated likelihood) of the target word gradually approaches the maximum belief strength of any word, as the target (which remains unknown until the end) appears in more and more cohorts.
We can also visualise the belief strength across the whole vocabulary at each guess, and the path the target word takes in relation to these distributions, in terms of its absolute score and its rank relative to other words.
The cohort solver can be (de)tuned to almost any level of performance by adjusting the parameters precision and recall, which determine the tightness of the similarity band and completeness of results from the generated cohorts. The gradient descent solver has potential for tuning parameters, but I didn’t explore this much. To compare the two, we’d therefore need to consider configurations of each solver. For now, I’m pleased that the two distinct sketches solve to my satisfaction!