27,949 research outputs found

    Quantum Lazy Sampling and Game-Playing Proofs for Quantum Indifferentiability

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    Game-playing proofs constitute a powerful framework for non-quantum cryptographic security arguments, most notably applied in the context of indifferentiability. An essential ingredient in such proofs is lazy sampling of random primitives. We develop a quantum game-playing proof framework by generalizing two recently developed proof techniques. First, we describe how Zhandry's compressed quantum oracles~(Crypto'19) can be used to do quantum lazy sampling of a class of non-uniform function distributions. Second, we observe how Unruh's one-way-to-hiding lemma~(Eurocrypt'14) can also be applied to compressed oracles, providing a quantum counterpart to the fundamental lemma of game-playing. Subsequently, we use our game-playing framework to prove quantum indifferentiability of the sponge construction, assuming a random internal function

    Deep learning for video game playing

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    In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

    Approximate Convex Optimization by Online Game Playing

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    Lagrangian relaxation and approximate optimization algorithms have received much attention in the last two decades. Typically, the running time of these methods to obtain a ϵ\epsilon approximate solution is proportional to 1ϵ2\frac{1}{\epsilon^2}. Recently, Bienstock and Iyengar, following Nesterov, gave an algorithm for fractional packing linear programs which runs in 1ϵ\frac{1}{\epsilon} iterations. The latter algorithm requires to solve a convex quadratic program every iteration - an optimization subroutine which dominates the theoretical running time. We give an algorithm for convex programs with strictly convex constraints which runs in time proportional to 1ϵ\frac{1}{\epsilon}. The algorithm does NOT require to solve any quadratic program, but uses gradient steps and elementary operations only. Problems which have strictly convex constraints include maximum entropy frequency estimation, portfolio optimization with loss risk constraints, and various computational problems in signal processing. As a side product, we also obtain a simpler version of Bienstock and Iyengar's result for general linear programming, with similar running time. We derive these algorithms using a new framework for deriving convex optimization algorithms from online game playing algorithms, which may be of independent interest

    EvoTanks: co-evolutionary development of game-playing agents

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    This paper describes the EvoTanks research project, a continuing attempt to develop strong AI players for a primitive 'Combat' style video game using evolutionary computational methods with artificial neural networks. A small but challenging feat due to the necessity for agent's actions to rely heavily on opponent behaviour. Previous investigation has shown the agents are capable of developing high performance behaviours by evolving against scripted opponents; however these are local to the trained opponent. The focus of this paper shows results from the use of co-evolution on the same population. Results show agents no longer succumb to trappings of local maxima within the search space and are capable of converging on high fitness behaviours local to their population without the use of scripted opponents

    Ensemble decision systems for general video game playing

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    Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem. Each individual algorithm has its own inherent strengths and weaknesses, and often it is difficult to overcome the weaknesses while retaining the strengths. Instead of altering the properties of the algorithm, the Ensemble Decision System augments the performance with other algorithms that have complementing strengths. This work outlines different options for building an Ensemble Decision System as well as providing analysis on its performance compared to the individual components of the system with interesting results, showing an increase in the generality of the algorithms without significantly impeding performance.Comment: 8 Pages, Accepted at COG201
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