Optimal Turn-Based Monster Battle Simulation with Ordering and Revival Decisions

Problem: Optimal Turn-Based Monster Battle Simulation (Ordering + Revival Decisions) You have two monster teams A and B. Each monster has: hp (positive integer) atk (positive integer) optionally special abilities (exact rules provided in the interview) Core Rules (build an extensible simulator) The battle proceeds in turns (who attacks first / simultaneous attacks depends on the provided rules). The two active monsters attack, reducing the opponent’s HP. When a monster’s hp <= 0, it is defeated and removed. Controllable Decisions You can choose a strategy to maximize win probability, including: Deployment order: choose the order in which your monsters enter battle (and possibly when to swap, if allowed). Revival decisions: under certain conditions, defeated monsters may be revived; you decide whether/when/which to revive (if applicable). Extension: Revival Mechanism A revival rule exists (e.g., revive at partial HP, limited number of revives, or skill-triggered revival). Your implementation must support it and incorporate revival decisions into the search. Goal Implement an algorithm to find an optimal strategy maximizing overall win probability. Requirements: Fully simulate all possible battle paths and revival decisions (search the strategy space). Provide an efficient solution via state representation, pruning, memoization, etc. Suggested I/O Input: two teams and parameters describing abilities/revival rules. Output: maximum win probability (or win/lose in deterministic settings), optionally also an optimal strategy. Sample Tests (Illustrative) Because exact rules were not specified in the post, these tests are structural: Deterministic fight with no revival. One-time revival available. Multiple monsters where order matters. Example Input (illustrative) A: [(hp=3,atk=2)] B: [(hp=4,atk=1)] no revival Output deterministic win/lose per rules