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Use a combination of Doddle's heuristic solvers and recursion to find the optimal tree.

The naming here is not good. is_common_word is used throughout the code as an indication that the word is one of the the two thousand words that could ever be a solution.

However, the meaning within the code has changed over time and now indicates whether a word is still a potential solution. I.e. is it part of a list of words that shrinks with every iteration.

Proposal - at all but the highest levels, rename to is_potential_soln.

• Expand the benchmarker to return more information
• Update the Doddle facade to allow for benchmarking
• Consider creating a HTML repr of the benchmark result object
• Add a convenience method for creating a directed graph
• Update packaging to allow for optional dependency on GraphViz
• Update documentation on how to install GraphViz
• Update Notebook to show how to benchmark
• Save SVG in images

• Create a SharedMemory buffer to ensure that the large ScoreMatrix class can be shared across processes with copying.
• Increase chunk size to reduce the overhead of creating new processes

Currently, the implementation of DeepMinimax returns the best depth1 guess associated with worst depth 2 outcome. However, this will not work for depths of 3+. Suppose that at depth 3+, the solver realises it is essential to return some guess, Guess A, at depth 2 in order to avoid major problems. The depth 1 solver would have no idea that it needs to play Guess A because the knowledge learnt at depth three is not being conveyed back up the chain.

Deep entropy doesn't have this problem because it modifies the guess. This is probably an acceptable workaround although an alternative is that deep guesses have a list (of length n) corresponding to the worst outcome at each depth n.

The introduction of a generic solver simplified the deep solvers materially. However, a few new errors have emerged (undetected for now) when running strict mypy. The engine must declare the generics associated with the solver.

Solution: make the solver covariant and fix the factory type annotations

The wordlebot leaderboard has a csv txt format for uploading your scores.

1. Ensure that Doddle can write the required format
2. Ensure Doddle can read the required format and generate a decision tree

Two ways this can occur:

1. If you provide invalid scores during Solve
2. if the solution in Run is impossible

Currently the solve CLI is a litttle... sad.

All the analytics are there, so let's make the UX a bit more fun. Provide more words, associated metrics and the remaining list of potential solutions.

Hint: sorted(solver.all_guesses()) gives what we need.

Because max(...) is not enough when there is only a histogram of ones. Need to choose the histogram bucket that doesn't hold the guess!

RAISE
BLUDY
CHUMP
CHUCK