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As the realism in games continues to increase, through improvements in graphics and 3D engines, more focus is placed on the behavior of the simulated agents that inhabit the simulated worlds. The agents in modern video games must become more life-like in order to seem to belong in the environments they are portrayed in. Many modern artificial intelligence approaches achieve a high level of realism...
In this paper, we present a version of the probabilistic neural network (PNN) that is capable of operating on a distributed database that is horizontally partitioned. It does so in a way that is privacy-preserving: that is, a test point can be evaluated by the algorithm without any party knowing the data owned by the other parties. We present an analysis of this algorithm from the standpoints of security...
In this paper, we present three different methods for implementing the probabilistic neural network on a Beowulf cluster computer. The three methods, parallel full training set (PFT-PNN), parallel split training set (PST-PNN) and the pipelined PNN (PPNN) all present different performance tradeoffs for different applications. We present implementations for all three architectures that are fully equivalent...
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