Incombinatorial game theory, theparanoid algorithm is agame treesearch algorithm designed to analyzemulti-player games using a two-player adversarial framework.[1] The algorithm assumes all opponents form a coalition to minimize the focal player’s payoff, transforming ann-playernon-zero-sum game into azero-sum game between the focal player and the coalition.
The paranoid algorithm significantly improves upon themaxn algorithm by enabling the use ofalpha-beta pruning and otherminimax-based optimization techniques that are less effective in standard multi-player game analysis.[2] By treating opponents as a unified adversary whose payoff is the opposite of the focal player’s payoff, the algorithm can applybranch and bound techniques and achieve substantial performance improvements over traditional multi-player algorithms.[3]
While the paranoid assumption may not accurately reflect the truestrategic interactions in all multi-player scenarios—where players typically optimize their own payoffs—the algorithm has proven effective in practice forartificial intelligence applications inboard games and other combinatorial multi-player games.[3] The algorithm is particularly valuable incomputer game AI where computational efficiency is crucial and the simplified opponent model provides adequate performance for real-time applications.
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