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Movements, leaders, victories and the continuing fight for equality.
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Explore the people, places, events, achievements, struggles and stories that shaped our journey.
Movements, leaders, victories and the continuing fight for equality.
Innovation, patents, science, technology and world-changing contributions.
Pioneers, champions, Negro Leagues, records, activism and excellence.
Meet the people whose lives, choices and achievements shaped the journey.
Black towns, communities, institutions and places where history happened.
Moments that changed communities, movements, institutions and the nation.
Wilmington, North Carolina once had a thriving Black middle class and an elected interracial government. In 1898 white supremacists used violence to overthrow that government, kill Black residents and drive many others from the city.
MORE →Reflects the personal views, recollections, and perspective of the author, Mike Davis.
This is a personal recollection on the Move fire on May 13, 1985
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.[1] 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.[2] 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.[3]
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.[3] 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.
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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. 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. 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. 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.
In graph theory, the Stoer–Wagner algorithm is a recursive algorithm to solve the minimum cut problem in undirected weighted graphs with non-negative weights. It was proposed by Mechthild Stoer and Frank Wagner in 1995. The essential idea of this algorithm is to shrink the graph by merging the most intensive vertices, until the graph only contains two combined vertex sets. At each phase, the algorithm finds the minimum s {\displaystyle s} - t {\displaystyle t} cut for two vertices s {\displaystyle s} and t {\displaystyle t} chosen at its will. Then the algorithm shrinks the edge between s {\displaystyle s} and t {\displaystyle t} to search for non s {\displaystyle s} - t {\displaystyle t} cuts. The minimum cut found in all phases will be the minimum weighted cut of the graph. A cut is a partition of the vertices of a graph into two non-empty, disjoint subsets. A minimum cut is a cut for which the size or weight of the cut is not larger than the size of any other cut. For an unweighted graph, the minimum cut would simply be the cut with the least edges. For a weighted graph, the sum of all edges' weight on the cut determines whether it is a minimum cut. In practice, the minimum cut problem is always discussed with the maximum flow problem, to explore the maximum capacity of a network, since the minimum cut is a bottleneck in a graph or network.
Minimax (sometimes Minmax, MM or saddle point) is a decision rule used in artificial intelligence, decision theory, combinatorial game theory, statistics, and philosophy for minimizing the possible loss for a worst case (maximum loss) scenario. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain. Originally formulated for several-player zero-sum game theory, covering both the cases where players take alternate moves and those where they make simultaneous moves, it has also been extended to more complex games and to general decision-making in the presence of uncertainty.
In game theory, an n-player game is a game which is well defined for any number of players. This is usually used in contrast to standard 2-player games that are only specified for two players. In defining n-player games, game theorists usually provide a definition that allow for any (finite) number of players. The limiting case of n → ∞ {\displaystyle n\to \infty } is the subject of mean field game theory. Changing games from 2-player games to n-player games entails some concerns. For instance, the Prisoner's dilemma is a 2-player game. One might define an n-player Prisoner's Dilemma where a single defection results everyone else getting the sucker's payoff. Alternatively, it might take certain amount of defection before the cooperators receive the sucker's payoff. (One example of an n-player Prisoner's Dilemma is the Diner's dilemma.)
Before the 1921 destruction of Tulsa’s Greenwood District, Black residents had created a remarkable center of business and community life. The district included stores, professional offices, entertainment venues and homes owned by Black citizens. Understanding Greenwood means learning what was built—not only what was burned.
MORE →Shirley Chisholm, elected in 1968.
