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Related Concept Videos

Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Rminimax: An Optimally Randomized MINIMAX Algorithm.

Silvia García Díez, Jérôme Laforge, Marco Saerens

    IEEE Transactions on Cybernetics
    |August 16, 2012
    PubMed
    Summary
    This summary is machine-generated.

    Introducing Rminimax, a novel algorithm that enhances the MINIMAX approach for games. Rminimax controls artificial intelligence (AI) opponent strength by optimizing strategy randomization, enabling bounded rationality in game AI.

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    Area of Science:

    • Artificial Intelligence
    • Game Theory
    • Computational Game Theory

    Background:

    • The MINIMAX algorithm is a cornerstone for decision-making in zero-sum two-player games.
    • Existing AI opponents often assume complete rationality, limiting adaptability.
    • Controlling AI opponent strength is crucial for realistic game simulations and training.

    Purpose of the Study:

    • To introduce Rminimax, an extension of the MINIMAX algorithm.
    • To enable controlled randomization of AI strategies for adjustable opponent strength.
    • To implement bounded rationality within game AI.

    Main Methods:

    • Extension of the MINIMAX algorithm with a randomized shortest-path framework.
    • Biasing AI adversary toward specific solution qualities through strategy randomization.
    • Balancing exploration (tree entropy) and exploitation (expected game cost) for optimal tradeoffs.
    • Efficient computation via a simple recurrence relation, maintaining MINIMAX complexity.

    Main Results:

    • Rminimax effectively controls the strength of AI opponents in games.
    • The algorithm introduces bounded rationality, moving beyond complete rationality assumptions.
    • Simulations on board games confirm the expected behavior and effectiveness of Rminimax.

    Conclusions:

    • Rminimax provides a principled method for creating nondeterministic, strength-adapted AI opponents.
    • This approach enhances game AI by incorporating adjustable difficulty levels.
    • The algorithm offers a computationally efficient way to implement bounded rationality in game theory.