Paranoid algorithm

{{Short description|Algorithm in game theory}}

In combinatorial game theory, the paranoid algorithm is a game tree search algorithm designed to analyze multi-player games using a two-player adversarial framework.{{cite journal |last1=Sturtevant |first1=Nathan |last2=Korf |first2=Richard |title=On Pruning Techniques for Multi-Player Games |date=30 July 2000 |pages=201–207 |journal=AAAI-00 Proceedings|url=https://cdn.aaai.org/AAAI/2000/AAAI00-031.pdf}} The algorithm assumes all opponents form a coalition to minimize the focal player’s payoff, transforming an n-player non-zero-sum game into a zero-sum game between the focal player and the coalition.

The paranoid algorithm significantly improves upon the maxⁿ algorithm by enabling the use of alpha-beta pruning and other minimax-based optimization techniques that are less effective in standard multi-player game analysis.Sturtevant and Korf, 2000 By treating opponents as a unified adversary whose payoff is the opposite of the focal player’s payoff, the algorithm can apply branch and bound techniques and achieve substantial performance improvements over traditional multi-player algorithms.{{cite book | last=Sturtevant | first=Nathan | title=Lecture Notes in Computer Science | chapter=A Comparison of Algorithms for Multi-player Games | publisher=Springer Berlin Heidelberg | publication-place=Berlin, Heidelberg | date=2003 | volume=2883 | isbn=978-3-540-20545-6 | doi=10.1007/978-3-540-40031-8_8 | pages=108–122}}

While the paranoid assumption may not accurately reflect the true strategic interactions in all multi-player scenarios—where players typically optimize their own payoffs—the algorithm has proven effective in practice for artificial intelligence applications in board games and other combinatorial multi-player games. The algorithm is particularly valuable in computer game AI where computational efficiency is crucial and the simplified opponent model provides adequate performance for real-time applications.