Composition of basic heuristics for the game 2048

2048 is a simple and intriguing sliding block puzzle game that has been studied for several years. Many complex solvers, often developed using neural nets are available and capable of achieving very high scores. We are, however, interested in using only basic heuristics, the kind that could be conceivably employed by human players without the aid of computation. A common way to implement a 2048 solver involves searching the game tree for the best moves, choosing a move and scoring the game board using some evaluation functions. The choice in heuristic evaluation function can dramatically affect the moves chosen by the solver. Furthermore, two or more possible moves can frequently produce the same score as evaluated by the same heuristic function, requiring either a random choice, or the use of a secondary or back up evaluation function which itself in turn may produce a tie. In this paper, we test the effectiveness of several basic heuristics in a simple 2048 solver. In order to test these, we create a system that takes basic predefined heuristic evaluation functions as input parameters, generates compositions from these functions with certain rules, and automatically tests all of them with a specified number of games. We find that compositions of evaluation functions that maximize empty spaces and monotonicity of tiles on the board-especially those that prioritize high numbers of empty spaces above prioritizing higher monotonicity- perform the best out of all compositions that we test.