Clarify the problem constraints and simulation rules first, then model it as an optimization problem where burning rows/columns is a decision that affects future infections. Propose a dynamic programming or greedy approach with state representing burned rows/columns and current infected set, and analyze time complexity.
Pro tip: Discuss trade-offs between exact optimality and computational feasibility; for large grids, suggest heuristic or approximation algorithms and explain why exact solution is NP-hard.
Ask about grid size, infection/recovery/death probabilities, whether burns are permanent, and if multiple burns can be done per day. Confirm the objective: minimize total deaths over entire simulation.
Represent the state as (day, burned rows, burned columns, infected cells). Each day, choose to burn a row/column or do nothing, then simulate infections/recoveries/deaths. Use BFS/DFS with memoization to find optimal sequence.
If grid is small, use DP over subsets of rows/columns burned. For larger grids, note that the problem is likely NP-hard and propose a greedy heuristic: burn rows/columns with highest current infection count.
Discuss time/space complexity of exact DP (exponential in rows+columns) versus greedy (polynomial). Suggest approximation guarantees or Monte Carlo simulation for stochastic elements.
Walk through a small example to verify the algorithm, and consider edge cases like all plants infected initially, no infections, or burning all rows/columns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.