Zarco Mario, Froese Tom
Departamento de Ciencias de la Computación, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Front Robot AI. 2018 Aug 21;5:96. doi: 10.3389/frobt.2018.00096. eCollection 2018.
A recent advance in complex adaptive systems has revealed a new unsupervised learning technique called self-modeling or self-optimization. Basically, a complex network that can form an associative memory of the state configurations of the attractors on which it converges will optimize its structure: it will spontaneously generalize over these typically suboptimal attractors and thereby also reinforce more optimal attractors-even if these better solutions are normally so hard to find that they have never been previously visited. Ideally, after sufficient self-optimization the most optimal attractor dominates the state space, and the network will converge on it from any initial condition. This technique has been applied to social networks, gene regulatory networks, and neural networks, but its application to less restricted neural controllers, as typically used in evolutionary robotics, has not yet been attempted. Here we show for the first time that the self-optimization process can be implemented in a continuous-time recurrent neural network with asymmetrical connections. We discuss several open challenges that must still be addressed before this technique could be applied in actual robotic scenarios.
复杂自适应系统的一项最新进展揭示了一种名为自建模或自优化的新型无监督学习技术。基本上，一个能够对其收敛的吸引子的状态配置形成关联记忆的复杂网络会优化其结构：它会自发地对这些通常次优的吸引子进行泛化，从而也强化更优的吸引子——即便这些更好的解决方案通常极难找到，以至于此前从未被发现过。理想情况下，经过充分的自优化后，最优的吸引子会主导状态空间，并且网络将从任何初始条件收敛到该吸引子上。这项技术已应用于社交网络、基因调控网络和神经网络，但尚未尝试将其应用于进化机器人学中通常使用的限制较少的神经控制器。在此我们首次表明，自优化过程可以在具有非对称连接的连续时间递归神经网络中实现。我们讨论了在将该技术应用于实际机器人场景之前仍需解决的几个开放性挑战。
