![]() you limit computer search time to 3 seconds max - an irrational opponent may cause your agent's performance to degrade to the point where it too starts to make mistakes. If the opponent manages to control play into a state where the heuristics are not accurate, they could cause minimax search to fail and misdirect your agent into making mistakes.Ī combination of effects is also possible if you have implemented a tree caching mechanism for performance improvements and made the system playable by limiting planning time - e.g. You will be relying on some heuristic to guide the minimax search when it cannot force an end game win. ![]() ![]() In these games, you will not have a truly optimal agent, but approximately optimal. This might impact decision time if you keep some partial game tree or cache branch evaluations between moves to help speed up the agent.Īctual optimal agents for games as complex as chess are not possible. pruning game tree segments that lead to non-optimal decisions by either player - might not be as useful. What it may mean is that optimisations you may have taken - e.g. In fact, if the opponent makes a mistake, often that can make the search easier/faster, and the agent will win more convincingly. Optimal play is such that the opponent's decision causes least impact to your agent. If your search is deep enough to guarantee optimal play in all cases, then yes. What happens if the opponent plays irrationally or sub-optimally? Do you still have a guarantee that you are going to win?
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