performance - Iterative deepening or trial and error? -

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i coding board game. have generated game tree using alpha-beta pruning , have 2 options:

  1. use iterative deepening optimize alpha-beta keeps generating 1 more ply until time over.
  2. by trial , error, know maximum depth reachable every board configuration in time limit without inspecting lower plies.

which approach better , make search reach deeper depth? know, example, @ beginning can generate tree of depth x consuming time available... can iterative deepening add more depth?

let me know if can more clear...

if branching factor , evaluation function's required time stays same between different rounds might able use option 2. sounds might difficult.

option 1 has ability dramatically increase alpha-beta's performance if don't have perfect move ordering already. save best move @ every node, found during previous search d-1, , tell alpha-beta search first. it's called transposition table, , if possible implement in case option 1 dominate option 2.

the first time heard of transposition table didn't think make of difference, increased maximum search depth 50%.


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