基于元胞自动机的联结主义符号机器智能--超维计算
Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing
摘要：
我们介绍了一个新的储备池计算，这是能够连接机器智能和符号计算。元胞自动机被用作动力系统的储存器。输入被随机地投射到自动机单元的初始条件上，并且通过在自动机中应用规则在一段时间内对输入执行非线性计算。自动机的演化创建了自动机状态空间的时空体积，它被用作水库。所提出的框架是能够长短期记忆，它需要的数量级更少的计算相比，回声状态网络。我们证明了元胞自动机水库持有一个分布式表示的属性统计，这提供了一个更有效的计算比本地表示。在支持向量机框架下，通过度量学习估计线性元胞自动机的核是可能的，这使得距离计算更加有效。此外，二元储层特征向量可以使用如在多维计算中的布尔运算来组合，为概念构建和符号处理铺平了直接的道路。
英文摘要：
We introduce a novel framework of reservoir computing, that is capable of both connectionist machine intelligence and symbolic computation. Cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is capable of long short-term memory and it requires orders of magnitude less computation compared to Echo State Networks. We prove that cellular automaton reservoir holds a distributed representation of attribute statistics, which provides a more effective computation than local representation. It is possible to estimate the kernel for linear cellular automata via metric learning, that enables a much more efficient distance computation in support vector machine framework. Also, binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
