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  2. 100 prisoners problem - Wikipedia

    en.wikipedia.org/wiki/100_prisoners_problem

    To describe the strategy, not only the prisoners, but also the drawers, are numbered from 1 to 100; for example, row by row starting with the top left drawer. The strategy is now as follows: [3] Each prisoner first opens the drawer labeled with their own number. If this drawer contains their number, they are done and were successful.

  3. Secretary problem - Wikipedia

    en.wikipedia.org/wiki/Secretary_problem

    Graphs of probabilities of getting the best candidate (red circles) from n applications, and k/n (blue crosses) where k is the sample size. The secretary problem demonstrates a scenario involving optimal stopping theory [1] [2] that is studied extensively in the fields of applied probability, statistics, and decision theory.

  4. How to Solve It - Wikipedia

    en.wikipedia.org/wiki/How_to_Solve_It

    The skill at choosing an appropriate strategy is best learned by solving many problems. You will find choosing a strategy increasingly easy. A partial list of strategies is included: Guess and check [9] Make an orderly list [10] Eliminate possibilities [11] Use symmetry [12] Consider special cases [13] Use direct reasoning; Solve an equation ...

  5. Game theory - Wikipedia

    en.wikipedia.org/wiki/Game_theory

    This research usually focuses on particular sets of strategies known as "solution concepts" or "equilibria". A common assumption is that players act rationally. In non-cooperative games, the most famous of these is the Nash equilibrium. A set of strategies is a Nash equilibrium if each represents a best response to the other strategies.

  6. Greedy algorithm - Wikipedia

    en.wikipedia.org/wiki/Greedy_algorithm

    For example, a greedy strategy for the travelling salesman problem (which is of high computational complexity) is the following heuristic: "At each step of the journey, visit the nearest unvisited city." This heuristic does not intend to find the best solution, but it terminates in a reasonable number of steps; finding an optimal solution to ...

  7. Multi-armed bandit - Wikipedia

    en.wikipedia.org/wiki/Multi-armed_bandit

    A row of slot machines in Las Vegas. In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K-[1] or N-armed bandit problem [2]) is a problem in which a decision maker iteratively selects one of multiple fixed choices (i.e., arms or actions) when the properties of each choice are only partially known at the time of allocation, and may become better ...

  8. Travelling salesman problem - Wikipedia

    en.wikipedia.org/wiki/Travelling_salesman_problem

    The following are some examples of metric TSPs for various metrics. In the Euclidean TSP (see below), the distance between two cities is the Euclidean distance between the corresponding points. In the rectilinear TSP, the distance between two cities is the sum of the absolute values of the differences of their x- and y-coordinates.

  9. Monty Hall problem - Wikipedia

    en.wikipedia.org/wiki/Monty_Hall_problem

    For example, assume the contestant knows that Monty does not open the second door randomly among all legal alternatives but instead, when given an opportunity to choose between two losing doors, Monty will open the one on the right. In this situation, the following two questions have different answers: