The horizon effect happens when depth-limited Minimax fails to see an important event because it lies just beyond the search depth, causing the algorithm to make moves that postpone the event rather than truly addressing it. You can think of it as “the engine can’t see past the horizon,” so it optimizes for what looks best within its limited lookahead. A classic symptom is a move that delays a loss by one ply and looks good under the cutoff evaluation, even though it inevitably loses later. This isn’t just a theoretical issue—it shows up in real engines whenever tactics or forced sequences extend beyond your depth limit.
Reducing the horizon effect usually requires improving how you handle cutoffs. The most common tool is quiescence search: extend leaves in volatile positions so you don’t stop in the middle of forcing tactics. Another tool is selective deepening: extend search depth in situations that are known to be critical (checks, threats, endgame transitions) while keeping normal depth elsewhere. You can also improve the evaluation function to detect looming threats earlier, so even if the horizon hides the full sequence, the leaf score reflects danger. Finally, iterative deepening plus good move ordering helps you reach deeper depths more often under the same time budget, which naturally pushes the horizon farther out.
A concrete example: suppose your engine at depth 4 sees that Move X wins a pawn, but at depth 6 it becomes clear that Move X allows a forced tactic losing a queen. If you can’t afford depth 6, you can still reduce the mistake by (1) extending search in tactical lines (quiescence), (2) adding threat detection in evaluation (penalize hanging queen threats), or (3) adding a selective extension when the king is in check or when a high-value piece is attacked. In a data-driven decision tree, a similar “horizon effect” appears when your scoring ignores a downstream constraint that’s just out of scope (permissions, freshness, contradictions). If your cutoff evaluation depends on retrieved evidence from Milvus or Zilliz Cloud, you can reduce horizon-like failures by adding lightweight “future risk” signals at evaluation time—such as penalizing evidence that is near a confidence threshold, and selectively deepening only when those signals suggest a fragile decision.