布尔问题分类器系统中尺度化的分层学习方法
A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems
摘要：
学习分类器系统（LCS）起源于认知科学研究，但迁移使得LCS成为强大的分类技术。现代LCS可用于提取知识的构建块，以解决相同或相关领域中更困难的问题。最近的工作表明，知识重用，通过采用代码片段，GP类树为基础的程序，到LCS可以提供扩展的进步。然而，由于解决难题通常需要构建高级构建块，这也会导致难以处理的搜索空间，因此最终将达到缩放的极限。受人类解决问题能力的启发，XCSCF* 可以重用学习到的知识和学习到的功能，通过使用分层学习将它们从简单的问题转移到复杂的问题。然而，这种方法是不完善的，只适用于多路复用器问题域。在本文中，我们提出了XCSCF* 的改进，使其能够在多个问题域的鲁棒性。这在基准多路复用器、进位一、多数决和偶奇偶校验域上得到了证明。提出了学习所需的基本公理，开发了LCS中的迁移学习方法，并将学习重新定义为一系列从属问题的分解。结果表明，从一个传统的白板，只有一个模糊的概念，什么从属问题可能是相关的，它可以捕捉测试域背后的一般逻辑，所以先进的系统是能够解决任何个人的n位多路复用器，n位进位一，n位多数，或n位偶奇偶校验问题。
英文摘要：
Learning classifier systems (LCSs) originated from cognitive-science research but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge to solve more difficult problems in the same or a related domain. Recent works on LCSs showed that the knowledge reuse through the adoption of Code Fragments, GP-like tree-based programs, into LCSs could provide advances in scaling. However, since solving hard problems often requires constructing high-level building blocks, which also results in an intractable search space, a limit of scaling will eventually be reached. Inspired by human problem-solving abilities, XCSCF* can reuse learned knowledge and learned functionality to scale to complex problems by transferring them from simpler problems using layered learning. However, this method was unrefined and suited to only the Multiplexer problem domain. In this paper, we propose improvements to XCSCF* to enable it to be robust across multiple problem domains. This is demonstrated on the benchmarks Multiplexer, Carry-one, Majority-on, and Even-parity domains. The required base axioms necessary for learning are proposed, methods for transfer learning in LCSs developed and learning recast as a decomposition into a series of subordinate problems. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, it is possible to capture the general logic behind the tested domains, so the advanced system is capable of solving any individual n-bit Multiplexer, n-bit Carry-one, n-bit Majority-on, or n-bit Even-parity problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
