A hallmark of intelligence is the ability to autonomously learn new flexible, cognitive behaviors - that is, behaviors where the appropriate action depends not just on immediate stimuli (as in simple reflexive stimulus-response associations), but on contextual information that must be adequately acquired, stored and processed for each new instance of the task. Artificial agents can learn such cognitive tasks with external, human-designed meta-learning (``learning-to-learn'') algorithms. By contrast, animals are able to pick up such cognitive tasks automatically, from stimuli and rewards alone, through the operation of their own evolved internal machinery. Can we harness this process to generate artificial agents with such abilities? Here we evolve neural networks, endowed with plastic connections and neuromodulation, over a sizable set of simple cognitive tasks adapted from a computational neuroscience framework. The actual weight modification process is entirely under control of the network itself, rather than being guided by an external algorithm. The resulting evolved networks can automatically modify their own connectivity to acquire novel simple cognitive tasks, never seen during evolution, from stimuli and rewards alone, through the spontaneous operation of their evolved neural organization and plasticity system. Our results emphasize the importance of carefully considering the multiple learning loops involved in the emergence of intelligent behavior.
翻译:智能的一个特征是自主学习新的灵活认知行为的能力 — 也就是说,当适当的行动不仅依赖于即时刺激(如简单的反反射刺激反应协会),而且取决于每个新任务中必须充分获取、储存和处理的背景资料。人工代理商可以通过外部、人为设计的元学习(“学习到学习”到“学习”)算法来学习这种认知任务。相比之下,动物能够自动地从自身进化的内部机器的运作中从刺激和奖赏中获取这样的认知任务。我们能否利用这个过程来产生具有这种能力的人工剂?在这里,我们发展有塑料连接和神经调节的神经网络,超越从计算神经科学框架中调整的一套可扩展的简单认知任务。实际重量调整过程完全在网络本身的控制之下,而不是受外部算法的指导。 由此形成的网络可以自动地改变自己的连接,通过进化过程中从未看到过的新简单的简单认知任务。 我们能利用这个过程来创造出这种能力来产生人工剂? 我们在这里,我们发展了神经网络,拥有塑料连接和神经调节, 超越了它们进化的机变的系统, 以及演化的造中, 思考我们的智能循环。