Context-dependent predictions and cognitive arm control with XCSF
Context-dependent predictions and cognitive arm control with XCSF
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使用 XCSF 进行上下文相关预测和认知手臂控制
DOI：
10.1145/1389095.1389360
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
O. Herbort
中科院分区：
文献类型：
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作者：
Martin Volker Butz;O. Herbort
虽然John Holland一直将学习分类器系统（LCS）视为认知系统，但大多数关于LCS的工作都集中在分类，数据挖掘和函数逼近上。在本文中，我们表明，XCSF分类器系统可以非常适当地修改，以控制机器人系统与冗余度的自由，如机器人arm.Inspired最近的研究见解，表明感觉运动代码几乎无处不在的大脑和认知的基本成分一般，XCSF系统被修改为学习分类器，编码分段线性感觉运动结构，其以预测相关的上下文输入为条件。在调查的机器人手臂的问题，我们表明，XCSF分区（上下文）的姿势空间的手臂，以这样一种方式，准确的手的动作可以预测特定的电机命令。此外，我们表明，反转的感觉运动预测结构，使准确的目标导向的闭环控制手臂达到运动。除了机器人手臂的应用，我们还研究了一组人工功能的修改后的XCSF系统的性能。所有的结果指出，XCSF是一个有用的工具，发展问题空间分区，最大限度地有效的感觉运动依赖性的编码。最后的讨论阐述了所采取的方法与实际大脑结构和认知心理学理论的学习和行为的关系。
While John Holland has always envisioned learning classifier systems (LCSs) as cognitive systems, most work on LCSs has focused on classification, datamining, and function approximation. In this paper, we show that the XCSF classifier system can be very suitably modified to control a robot system with redundant degrees of freedom, such as a robot arm. Inspired by recent research insights that suggest that sensorimotor codes are nearly ubiquitous in the brain and an essential ingredient for cognition in general, the XCSF system is modified to learn classifiers that encode piecewise linear sensorimotor structures, which are conditioned on prediction-relevant contextual input. In the investigated robot arm problem, we show that XCSF partitions the (contextual) posture space of the arm in such a way that accurate hand movements can be predicted given particular motor commands. Furthermore, we show that the inversion of the sensorimotor predictive structures enables accurate goal-directed closed-loop control of arm reaching movements. Besides the robot arm application, we also investigate performance of the modified XCSF system on a set of artificial functions. All results point out that XCSF is a useful tool to evolve problem space partitions that are maximally effective for the encoding of sensorimotor dependencies. A final discussion elaborates on the relation of the taken approach to actual brain structures and cognitive psychology theories of learning and behavior.