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摘要: 针对结构固定认知模型中存在的学习浪费与计算浪费问题, 在具有内发动机机制的感知行动认知模型基础上, 根据操作条件反射学习特性, 借鉴潜在动作原理, 建立起一种具有发育机制的感知行动认知模型D-SSCM (Development-sensorimotor cognitive model), 具体为一个14元组, 包含离散学习时间集、内部可感知离散状态集、可输出动作集、有效输出动作空间集、潜在动作关系集、可输出动作空间探索率集及发育算法等.针对模型发育过程, 分别设计了模型结构扩展式发育方法和算法以及缩减式发育方法和算法, 定义了模型的发育式学习过程.使用两轮机器人自平衡任务对设计的学习模型进行验证, 实验结果表明, 发育机制下的感知行动认知模型D-SSCM具有更快的学习速度及更稳定的学习效果.Abstract: Aiming at the problems of learning waste and computing waste that exist in the cognitive models with fixed structure, and according to the operate conditioning learning characteristics as well as drawing on the affordance theory, a new kind of sensorimotor cognitive model named D-SSCM with the developmental mechanism is established based on the sensorimotor cognitive model with the mechanism of intrinsic motivation. D-SSCM is a fourteen tuple in specific, including discrete learning time set, internal sensible discrete state set, optional motion set, effective output motion space set, affordance relationship set, optional motion space exploration rate set, developmental algorithm and etc. In view of D-SSCM's developmental learning, extended developmental method and algorithm as well as reduced developmental method and algorithm are designed. Model's developmental learning process is defined. Using two-wheeled robot self-balancing task to test this designed model, results show that D-SSCM is with faster learning speed and more stable learning effect.
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Key words:
- Development /
- operant conditioning /
- affordance /
- sensorimotor cognitive /
- two-wheeled robot
1) 本文责任编委 张俊 -
图 12 10轮学习中的$n_M$及$n_{M_\text{s}}$数
Fig. 12 $n_M$ and $n_{M_\text{s}}$ in 10 learning rounds
表 1 D-SSCM状态划分
Table 1 D-SSCM state division
$\varphi\, (^{\circ})$ $\dot{\varphi}\, (^{\circ}/s)$ $(-\infty, -17.5)$ $(-\infty, -100)$ $[-17.5, -12.5)$ $[-100, -50)$ $[-12.5, -7.5)$ $[-50, -20)$ $[-7.5, -2.5)$ $[-20, -5)$ $[-2.5, -0.5)$ $[-5, -2)$ $[-0.5, 0)$ $[-2, 0)$ $[0, 0.5)$ $[0, 2)$ $[0.5, 2.5)$ $[2, 5)$ $[2.5, 7.5)$ $[5, 20)$ $[7.5, 12.5)$ $[20, 50)$ $[12.5, 17.5)$ $[50, 100)$ $[17.5, +\infty)$ $[100, +\infty)$ 
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表 2 10轮学习中的$n_M$及$n_{M_{\rm s}}$数
Table 2 $n_M$ and $n_{M_{\rm s}}$ in 10 learning rounds
学习轮数 1 2 3 4 5 6 7 8 9 10 $M$空间感知行动映射探索次数 588 589 590 592 592 598 609 609 610 610 $M_{\rm s}$空间有效感知行动映射数 169 170 171 172 171 173 173 173 173 173
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