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Exploiting Open-Endedness to Solve Problems Through the Search for Novelty

机译：利用开放性解决新问题

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This paper establishes a link between the challenge of solving highly ambitious problems in machine learning and the goal of reproducing the dynamics of open-ended evolution in artificial life. A major problem with the objective function in machine learning is that through deception it may actually prevent the objective from being reached. In a similar way, selection in evolution may sometimes act to discourage increasing complexity. This paper proposes a single idea that both overcomes the obstacle of deception and suggests a simple new approach to open-ended evolution: Instead of either explicitly seeking an objective or modeling a domain to capture the open-endedness of natural evolution, the idea is to simply search for novelty. Even in an objective-based problem, such novelty search ignores the objective and searches for behavioral novelty. Yet because many points in the search space collapse to the same point in behavior space, it turns out that the search for novelty is computationally feasible. Furthermore, because there are only so many simple behaviors, the search for novelty leads to increasing complexity. In fact, on the way up the ladder of complexity, the search is likely to encounter at least one solution. In this way, by decoupling the idea of open-ended search from only artificial life worlds, the raw search for novelty can be applied to real world problems. Counterintuitively, in the deceptive maze navigation task in this paper, novelty search significantly outperforms objective-based search, suggesting a surprising new approach to machine learning.
机译：本文建立了在解决机器学习中高度雄心勃勃的问题所面临的挑战与在人造生活中再现开放式进化动力学目标之间的联系。机器学习中目标功能的一个主要问题是，通过欺骗，它实际上可能阻止达到目标。以类似的方式，进化中的选择有时可能会阻止增加的复杂性。本文提出了一个单一的想法，该想法既可以克服欺骗的障碍，又可以提出一种简单的开放式进化新方法：与其明确地寻找目标或建模域以捕获自然进化的开放性，不如说是只是寻找新颖性。即使在基于目标的问题中，这种新颖性搜索也会忽略目标，而是搜索行为新颖性。然而，由于搜索空间中的许多点都崩溃到行为空间中的相同点，因此事实证明，新颖性的搜索在计算上是可行的。此外，由于仅存在许多简单的行为，因此寻求新颖性会导致复杂性增加。实际上，在提高复杂性的过程中，搜索可能会遇到至少一种解决方案。通过这种方式，通过将开放式搜索的思想与人工世界分离开，新颖性的原始搜索可以应用于现实世界中的问题。与直觉相反，在本文的欺骗性迷宫导航任务中，新颖性搜索明显优于基于目标的搜索，这表明了一种令人惊讶的机器学习新方法。

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