Seconded. While this book has a lot of nice encouragements to simply coding and enjoying mazes, it also ships with two absolutely kick-ass appendices. The firs

Seconded. While this book has a lot of nice encouragements to simply coding and enjoying mazes, it also ships with two absolutely kick-ass appendices. The first is a very short bite-size summary of all of the algorithms that the book has covered, so that you don't have to read through the entire book again to remember the differences between the different algorithms. This is so good I wonder why it is not a standard feature of textbooks in general, such that it has its own standard section name (say "Abridgement"). The key is that it's longer-form than an introduction or abstract or conclusion, but the freedom to occupy its own space allows it to be expressed in human terms, unlike the "real research" parts which have to be a bit more rigorous. The second is a quantitative comparison of the different algorithms among different measurements, which again is like "why isn't this an appendix everywhere?" and such. It's still not trying to be absolutely rigorous but just to give a rough feel for the different options there. As a result you can just grab the textbook as a reference, flip through those appendices, say "yeah that's what I'm looking for, longer twistier paths," read up a quick description of how that algorithm works, say "well that's a bit more involved than this other one which looks good enough, maybe I'll start from there."