Hurst Jacob, Bull Larry
Faculty of Computing, Engineering & Mathematical Sciences, University of the West of England, Bristol, BS16 1QY, UK.
Artif Life. 2006 Summer;12(3):353-80. doi: 10.1162/artl.2006.12.3.353.
For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.
为了使人工实体实现真正的自主性并展现出复杂的逼真行为，它们将需要利用适当的可适应学习算法。在这种情况下，适应性意味着在任何给定时间由环境引导的灵活性以及学习适当行为的开放式能力。本文研究了在神经学习分类器系统架构中使用受建构主义启发的机制，该架构利用参数自适应作为实现此类行为的一种方法。该系统使用一种规则结构，其中每个规则由一个人工神经网络表示。结果表明，在学习过程中会以学习者控制的速率出现适当的内部规则复杂性，并且该结构表明了任务的潜在特征。在转向移动机器人平台之前，先在模拟迷宫中展示了结果。
