Learning in a small world
Learning in a small world
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DOI：
10.5555/2343576.2343632
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Arun Tejasvi Chaganty;P. Gaur;Balaraman Ravindran
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文献类型：
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作者：
Arun Tejasvi Chaganty;P. Gaur;Balaraman Ravindran
了解我们如何能够执行各种复杂的任务是人工智能社区的一个核心问题。一种流行的方法是使用时间抽象作为框架来捕获子任务的概念。然而，这将问题转移到寻找正确的子任务上，这仍然是一个悬而未决的问题。现有的子任务生成方法需要太多的环境知识，并且它们创建的抽象可能会让代理不知所措。我们提出了一种受小世界网络启发的简单算法，用于学习子任务，同时解决几乎不需要环境信息的任务。此外，我们还表明，我们学习的子任务可以很容易地由代理组成来解决任何其他任务；更正式地说，我们证明任何任务都可以仅使用这些子任务和原始动作的对数组合来解决。实验结果表明，我们生成的子任务优于标准域上其他流行的子任务生成方案。
Understanding how we are able to perform a diverse set of complex tasks is a central question for the Artificial Intelligence community. A popular approach is to use temporal abstraction as a framework to capture the notion of subtasks. However, this transfers the problem to finding the right subtasks, which is still an open problem. Existing approaches for subtask generation require too much knowledge of the environment, and the abstractions they create can overwhelm the agent. We propose a simple algorithm inspired by small world networks to learn subtasks while solving a task that requires virtually no information of the environment. Additionally, we show that the subtasks we learn can be easily composed by the agent to solve any other task; more formally, we prove that any task can be solved using only a logarithmic combination of these subtasks and primitive actions. Experimental results show that the subtasks we generate outperform other popular subtask generation schemes on standard domains.