摘要翻译：
本文提出了一种在连续和非预置环境中学习高冗余逆模型的算法。我们的社会引导的演示内在动机（SGIM-D）算法结合了社会学习和内在动机的优势，在减少对教师的依赖的同时，专注于广泛的技能。SGIM-D在一个钓鱼技能学习实验中进行了评价。
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英文标题：
《Constraining the Size Growth of the Task Space with Socially Guided
  Intrinsic Motivation using Demonstrations》
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作者：
Sao Mai Nguyen (INRIA Bordeaux - Sud-Ouest), Adrien Baranes (INRIA
  Bordeaux - Sud-Ouest), Pierre-Yves Oudeyer (INRIA Bordeaux - Sud-Ouest)
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  This paper presents an algorithm for learning a highly redundant inverse model in continuous and non-preset environments. Our Socially Guided Intrinsic Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both social learning and intrinsic motivation, to specialise in a wide range of skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a fishing skill learning experiment.
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PDF链接：
https://arxiv.org/pdf/1111.6790