Action-outcome contingencies as the engine of open-ended learning: computational models and developmental experiments

G. Baldassarre, Francesco Mannella, V. Santucci, E. Somogyi, Lisa Jacquey, Mollie Hamilton, J. O'Regan
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引用次数: 7

Abstract

Open-ended learning allows humans and robots to autonomously acquire an increasingly large repertoire of skills, that later can allow them to produce suitable actions to achieve desirable effects in the environment (‘goals'). Empirical evidence from developmental psychology suggests that a pivotal mechanism possibly driving open-ended learning is represented by action-outcome contingencies. Here we propose a specific hypothesis, expressed in the form of a blueprint cognitive architecture, that sketches the general mechanisms through which contingency-based open-ended learning might take place. According to this hypothesis, the matching (or distance) between a desired goal and the actual effect produced by the action can be used to drive the learning of both the motor skill used to accomplish the goal and the internal representation of the action outcome. We report here a computational model that implements the hypothesis and we illustrate two developmental psychology experiments related to the presented theory. Overall the model and experiments show the soundness of the hypothesis and represent a start towards validating it experimentally.
作为开放式学习引擎的行动-结果偶然性:计算模型和发展实验
开放式学习使人类和机器人能够自主地获得越来越多的技能,这些技能后来可以使他们产生适当的行动,以在环境中达到理想的效果(“目标”)。发展心理学的经验证据表明,开放式学习的关键机制可能是行动-结果偶然性。在这里,我们提出了一个具体的假设,以蓝图认知架构的形式表达,它概述了基于权变的开放式学习可能发生的一般机制。根据这一假设,期望目标与行动产生的实际效果之间的匹配(或距离)可以用来驱动完成目标所使用的运动技能的学习和行动结果的内部表征。我们在这里报告了一个实现这一假设的计算模型,并举例说明了与所提出的理论相关的两个发展心理学实验。总的来说,模型和实验表明了假设的合理性,并代表了实验验证它的开始。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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