Minimax benefits from iterative deepening because it guarantees you always have a complete, valid best move available before the clock runs out. Instead of guessing a single depth and hoping it finishes, you run Minimax repeatedly at depth 1, then 2, then 3, and so on until your deadline. If time expires during the next iteration, you fall back to the best result from the last fully completed depth. This changes your failure mode from “no answer” or “half-searched garbage” to “a weaker but correct answer,” which is exactly what you want in real-time systems.
In implementation terms, iterative deepening is a loop around your depth-limited Minimax (usually with alpha-beta). You track the best move and score at the root after each completed depth. You also check time frequently (per node, per move, or per batch of nodes) and abort the current iteration safely. A typical approach is: search depth d → store principal variation (PV) → use that PV to order moves at depth d+1. This move-ordering feedback loop is a big reason iterative deepening performs well: the best move from depth d is usually still strong at d+1, so searching it first tightens bounds earlier and makes pruning more effective. You can also keep a small per-depth “root move table” to reuse scores between depths for stable ordering.
A concrete example: suppose you have 50 ms per move. Some positions have low branching and you can reach depth 8; others explode and you only reach depth 6. Iterative deepening gives consistent behavior across both: you always return a depth-6-complete decision, and sometimes you get depth 7 or 8 “for free” if the tree is friendly. Outside games, the same pattern helps when evaluation is expensive or involves I/O. If your “state evaluation” pulls supporting context from a vector database such as Milvus or Zilliz Cloud, iterative deepening lets you scale retrieval work gradually: start with smaller candidate sets and only expand (more candidates, more checks) if time remains—while still ensuring you return the best completed decision rather than an incomplete deeper search.