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in AI and Deep Learning by (50.2k points)

Suggest an algorithm and data structure for solving the game Globs ( It's pretty fun in a geeky kind of way.

State the time-space complexity (big-O) of your approach in terms of N, the size of the grid (N>=14). Good-enough efficient algorithms with low complexity are preferred.

(MatrixFrog correctly points out this game is also known as FloodIt, and Smashery gave a solution 3 months ago in the link he cites below. All you dudes suggesting pruning/greedy with only 1 lookahead, that gives suboptimal solutions.)

The game generates a random square grid of nxn nodes, where each node is colored one of six colors (Grn=1, Ylw=2, Red=3, Blu=4, Pur=5, Orn=6). Level 1 has a 9x9 grid, then n increases each level, up to 14. Each level you can take up to 25 turns or else you lose. On each turn you choose which color to change the top left node to e.g. Grn->Red, such that any connected adjacent (horiz/vert) nodes of the new color get assimilated into shape, and 1 pt per node assimilated is ADDED to your score. The scoring objective is to complete each grid in as few turns as possible, e.g. if you do it in 16 turns, then your 9 unused moves => 2*9 MULTIPLIER times your total accumulated score.

Obviously, there are a ton of ways to decompose this, and the default choice of recursive backtracking with a 14x14 grid is a viable contender; What other types of data structures does this lend itself to? A *? Don't get hung up on optimality, I'm wondering if there is a "good enough" algorithm.

(I thought it might be a fun project to code up a robot and get silly-high scores. Although I scored 3.5E+12 all by my flesh were self.)

1 Answer

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When talking about A*, the effectiveness of it really boils down to your choice of heuristic estimation. The closer you get to guessing the actual distance, the fewer nodes that will have to be expanded in order to reach the goal. For this problem, I went through a number of ideas for estimation, but most of them broke the A* rule, which is that you can NOT overestimate the actual distance, or else you break the optimality of A*.

The problem is that A* is really only good when each step makes a significant refinement to the actual distance of the overall solution. Looking at the problem directly, you probably won't find a good heuristic that can do much better than this without overestimating the cost. However, if you transform the problem into another problem, better heuristics jump out at you. The heuristic "number of colors remaining" is answering the question, what is the smallest number of possible moves remaining. The answer to that question, I asked myself "which spot on the board requires the maximum number of steps to get to"? I ended up settling on the answer to "how many steps is it to the bottom right corner" for my heuristic. 

This is fairly easy to implement by running another A* search that works more like finding map directions and then counting the number of steps in the solution. I realize this is an arbitrary point on the board to select, however, it worked quite well in testing and running A* on every remaining point took a fair amount of time on my single processor test machine.

If you use color connectivity as your distance then there is a clear number of minimum steps to each node and you could use that distance in your heuristic.

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