I’m working on building an AI bot for the 2048 game and I want to use the minimax approach for decision making. The problem I’m running into is that 2048 doesn’t really have a traditional adversarial setup like other games.
In 2048, there’s no actual opponent trying to minimize my score. Instead, new tiles appear randomly on the board. This makes it more like an expectiminimax tree where I only have the maximizing player (me) and chance nodes (random tile placement), but no minimizing opponent.
Since alpha-beta pruning typically works by using the min-max bounds between opposing players, I’m not sure how to implement it effectively in this situation. Can alpha-beta pruning even be applied when there’s no minimizing player in the game tree?
If alpha-beta pruning isn’t the right approach here, what other techniques can I use to cut down on unnecessary branches and make the search more efficient? I want to avoid exploring parts of the tree that won’t help improve the AI’s performance.
Thanks for any suggestions or ideas you might have.
expectiminimax can still utilize pruning techniques like star1 pruning, which manages chance nodes with probability weighted bounds. also, consider using transposition tables to cache positions you’ve computed; you can reach the same 2048 board from different move sequences. it really saves computational time!
You’re right - alpha-beta pruning doesn’t work with expectiminimax trees since chance nodes mess with the min-max logic.
For 2048, try probability bounds pruning instead. Track expected value bounds at each node and cut branches that can’t beat your current best expected value.
Depth-limited search with iterative deepening works too. Set a depth limit and use heuristic evaluation for terminal nodes to keep search time reasonable.
Monte Carlo Tree Search (MCTS) is probably your best bet though. It handles the randomness naturally and focuses on the most promising moves.
Honestly, coding all this tree search stuff manually is tedious. I’ve used Latenode to automate similar AI workflows - it handles the optimization algorithms while you focus on game logic.
You can automate testing different pruning strategies, tune parameters, and deploy your bot through their visual workflow system. Way easier than coding everything from scratch.
Had the same problem with my 2048 solver last year. Skip alpha-beta and go with branch-and-bound plus heuristic pruning instead. Here’s what made it click: treat random tiles as probability distributions and keep upper confidence bounds for each subtree. If a branch’s best-case score drops below your current best, prune it. Move ordering is huge - check immediate merges first, then high-scoring combos. Gets you better solutions faster and triggers way more pruning. Pro tip: use different probability thresholds for 2-tiles vs 4-tiles since they don’t spawn at the same rate. Skip low-probability 4-tile spots when 2-tiles already show a clear winning path. Once you accept that perfect play isn’t the goal - just consistently beating random within reasonable compute time - the search becomes totally manageable.
Been down this exact road with a 2048 bot a few years back. You’re right - alpha-beta doesn’t work here.
Beam search + aggressive move ordering was my solution. Keep only the top N promising positions at each depth instead of exploring everything. Cut search space by 80% with barely any quality loss.
Get your evaluation function right first though. I used weighted empty tiles, monotonicity, and smoothness. Once that’s solid, you can prune aggressively since you trust your scoring.
Try expectimax with limited depth plus good heuristics at leaf nodes. Simpler than full expectiminimax but handles randomness properly.
One surprise - caching board states helps way more than expected. Same positions show up through different move sequences constantly in 2048.