Overcoming deception in evolution of cognitive behaviors
Overcoming deception in evolution of cognitive behaviors
复制标题
克服认知行为进化中的欺骗
DOI：
10.1145/2576768.2598300
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
R. Miikkulainen
中科院分区：
文献类型：
--
作者：
J. Lehman;R. Miikkulainen
当将神经进化扩展到复杂的行为时，学习、交流和记忆等认知能力变得越来越重要。然而，成功地进化这种认知能力仍然很困难。本文认为，造成这种困难的一个主要原因是欺骗，即进化收敛于与期望解决方案无关的行为。更具体地说，认知行为通常需要积累神经结构，而这种结构不会立即带来适应性益处，因此进化往往会趋同于非认知解决方案。为了研究这一假设，我们以三种不同的方式对一个常见的进化机器人T-Maze域进行了调整，以要求智能体进行沟通、记忆和学习。以目标为基础的适应性驱动的进化往往集中在简单的非认知行为上，这表明存在欺骗。相比之下，探索新奇行为的进化，即新颖性搜索，通常会进化出期望的认知行为。结论是，开放式的进化方法可能会更好地识别和奖励认知行为出现所必需的垫脚石。
When scaling neuroevolution to complex behaviors, cognitive capabilities such as learning, communication, and memory become increasingly important. However, successfully evolving such cognitive abilities remains difficult. This paper argues that a main cause for such difficulty is deception, i.e. evolution converges to a behavior unrelated to the desired solution. More specifically, cognitive behaviors often require accumulating neural structure that provides no immediate fitness benefit, and evolution often thus converges to non-cognitive solutions. To investigate this hypothesis, a common evolutionary robotics T-Maze domain is adapted in three separate ways to require agents to communicate, remember, and learn. Indicative of deception, evolution driven by objective-based fitness often converges upon simple non-cognitive behaviors. In contrast, evolution driven to explore novel behaviors, i.e. novelty search, often evolves the desired cognitive behaviors. The conclusion is that open-ended methods of evolution may better recognize and reward the stepping stones that are necessary for cognitive behavior to emerge.