Look-Ahead Algorithms

By now you’ve all heard the great news: Monkey has proved it is possible to beat Spider Solitaire at the 4-suit level! All he needs to do is build in-suit at every opportunity and never break a suited connector. The bad news is the win rate is extremely low. Of course if the Monkey wins $3000 from the Eagle thanks to a single victory then we’re not complaining – but can we do even better?

First I wanna differentiate between a random hand and a custom hand. Recall that I discussed a number of you-tube videos where random players show off their awesome skills by beating a hand of 4-suit spider sans 85,78,68,79, but the unmentioned caveat was the hand was ridiculously easy.

If I pick a ridiculously easy hand then Monkey wins 6 games in 500, or 1.2 percent of the time. If I pick a random hand then Monkey wins a <sarcasm> whopping </sarcasm> zero games in 500.

So far so bad.

It turned out my simulated annealing/stochastic hill climbing/stochastic gradient descent/something else thingy was a red herring. It worked the first time, but later experimentation with the random number generator showed that “always Level 3” was the best way to go in the long run. But at least it made for a good story lol 😊

Guaranteed Minimum Evaluation Score

Good players (or avid readers of this blog or both!) will be aware of the concept of minimum guaranteed turn-overs. Basically we can calculate the minimum number of cards we are guaranteed to turn over even if the worst possible cards showed up. Of course we can do something similar for e.g. the number of suited connectors or some other “function” of the position. For instance, we might assign 10 points for flipping a card, 1 point for a suited connector and 100 points for removing a complete suit. And of course experienced players know that different programs will have different scoring systems. The important point is that we can (i) evaluate a given position (ii) compute a guaranteed minimum score even if the worst possible cards turned up.

This is an example of “looking ahead” since we are considering the consequences of a single move given the available information (i.e. face-up cards), which is obviously better than making a single move because it’s an in-suit build.

Here is a simple example: suppose the evaluation function was 10 points for exposing a card, 1 point for any suited connector, and no 1-point penalty for each move. Assuming best play in the start position below, what is your Guaranteed Minimum Evaluation Score (GMES) even if you turned over the worst possible cards?

I hoped you answered 54. Note that we are lucky in the sense we can build in-suit 4 times and off-suit once. Normally it’s the other way around.

A good first move is to build in-suit with the Jack-Ten of diamonds. The Ten is on one of six columns with equal-fewest cards and we also have spare off-suit Jack. But we can make many other moves and still come to the same GMES of 54. You could start with the Three-Two of Clubs or even the strange looking 9h-8d. But obviously the sensible option (for humans at least!) is Jd-0d.

The position you should be visualising is the one below, where a happy star represents any face-up card. We can easily check with a single glance that there are 5 turnovers and 4 in-suit builds, with a total evaluation score of 54.

If we want to train our Ninja Monkey to compute the best GMES we can simply iterate random sequences of moves from a given position. But we must be careful not to expose any new cards (e.g. if we shift the Ten of diamonds we will not flip the card underneath). Assuming we have enough iterations, Ninja Monkey will deduce e.g. shifting the Jack of diamonds onto the Club Queen is not a good move since it reduces the GMES from 54 to 53. Even worse would be 0d-9h, which reduces the GMES to 42. We lose one guaranteed turn-over and two in-suit builds with one bad move!

An example of a “good  enough sequence” is:  Jc-0d, 9d-8d, 3c-2d, 0d-98d, Jd-098d, Qc-Jc. Note that the first move was unnecessary but at least we got the correct GMES of 54.

Now Get Off Yer 65,82,83,79 And Start Procrastinating!

A lazy programmer would be satisfied with the above algorithm but of course it is possible to do even better than that. Note that there is no reason to commit ourselves to every move in the above sequence without examining the newly-turned over cards. After starting with Jc-0d, we can turn over a card and we always have the choice of 9d-8d, 3c-2d, 0d-98d, Jd-098d, Qc-Jc or the new card may give us an even better option. In effect we are procrastinating. In summary Ninja Monkey will find a sequence with the best possible MGES and execute it, but terminating the sequence prematurely if he turns over at least one new card.

How do we tell if it’s time to deal a new row of cards? Simples, as the Meerkat would say. If Ninja Monkey cannot increase the GMES then it is time to deal a new row of cards. If the stock is empty then either the game is won or the Monkey concedes defeat.

With the new algorithm, Monkey will beat an easy hand around half the time or a random hand 5 percent of the time. Random Hand statistics are shown in the graphic below

Let us say that a Spider hand is ImportNumbPieAsNP-Hard if Ninja Monkey cannot obtain one victory in 50 tries. In this case roughly half the hands are ImportNumbPieAsNP-Hard. At the other end of the spectrum there was also one very easy hand with 21 wins in 50 attempts. One implication of all this is if a Spider Solitaire server gives 40 ImportNumbPieAsNP-Hard games in a row then there’s a pretty good chance it’s biased.

Unfortunately all this comes at a price. Despite Ninja Monkey’s extremely fast metabolism, this takes a very long time to run.

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