摘要翻译：
计算机科学的大部分关注于自动解决给定的计算问题。我专注于以一种受动物和人类嬉戏行为启发的方式自动发明或发现问题，以一种无人监督的方式从头开始训练一个越来越普遍的问题解决者。考虑具有可能可计算解的任务的所有可计算描述的无限集。新的算法框架POWERPLAY(2011)不断搜索可能的新任务对和当前问题求解器的修改空间，直到找到一个更强大的问题求解器，它可以证明解决所有以前学习的任务加上新的任务，而未修改的前一个不能。哇-效果是通过不断提高以前学习的技能的效率来实现的，这样它们需要更少的时间和空间。新技能可能（部分）重复使用以前学过的技能。PowerPlay的搜索根据其条件计算（时间和空间）复杂度对候选任务对和求解器修改进行排序，给定到目前为止存储的经验。新任务及其相应的任务解决技巧是首次发现和验证的任务。验证新任务的计算成本不需要随着任务汇辑的大小而增加。PowerPlay对新颖性的不断探索不断打破其当前求解器的泛化能力。这与哥德尔的一系列日益强大的形式理论有关，这些理论的基础是在公理中增加以前不可证明的陈述，而不影响以前可证明的定理。不断增加的问题解决程序可以通过并行搜索额外外部提出的任务的解决方案来利用。POWERPLAY可能被视为对创造性基本原则的贪婪但实际的实现。第一个实验分析可以在单独的论文中找到[53,54]。
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英文标题：
《POWERPLAY: Training an Increasingly General Problem Solver by
  Continually Searching for the Simplest Still Unsolvable Problem》
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作者：
J\"urgen Schmidhuber
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Most of computer science focuses on automatically solving given computational problems. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. The novel algorithmic framework POWERPLAY (2011) continually searches the space of possible pairs of new tasks and modifications of the current problem solver, until it finds a more powerful problem solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Wow-effects are achieved by continually making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. POWERPLAY's search orders candidate pairs of tasks and solver modifications by their conditional computational (time & space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. The computational costs of validating new tasks need not grow with task repertoire size. POWERPLAY's ongoing search for novelty keeps breaking the generalization abilities of its present solver. This is related to Goedel's sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of problem solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. POWERPLAY may be viewed as a greedy but practical implementation of basic principles of creativity. A first experimental analysis can be found in separate papers [53,54].
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PDF链接：
https://arxiv.org/pdf/1112.5309