机器人梦:情景记忆事后处理的计算论证

T. Kelley
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引用次数: 24

摘要

作为符号和亚符号机器人智能控制系统(SS-RICS)开发的一部分,我们已经实现了一个记忆存储,允许机器人从以前的经验中保留知识。作为开发事件记忆存储的一部分,对离线、无意识的提示过程进行了测试。比较了三种识别前事件的策略。第一种策略存储所有记忆,并在所有记忆中搜索与当前事件匹配的记忆。第二种策略是在事件发生时搜索记忆,并首先从最近的记忆开始搜索。最后,第三种策略使用修剪、抽象和线索对所有记忆进行后处理。修剪去除记忆,抽象使用类别来减少度量信息,提示过程为随后的情节识别提供指针。我们发现将后处理记忆作为一个无意识的过程是最有效的策略。这种计算实现为存储器的后处理作为一种有效的存储器检索手段提供了理由。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robotic Dreams: A Computational Justification for the Post-Hoc Processing of Episodic Memories
As part of the development of the Symbolic and Sub-symbolic Robotics Intelligence Control System (SS-RICS), we have implemented a memory store to allow a robot to retain knowledge from previous experiences. As part of the development of the event memory store justification for an off-line, unconscious, cueing process was tested. Three strategies for the recognition of previous events were compared. The first strategy stored all memories and searched all of the memories for a match to the current event. The second strategy searched memories while an event was taking place and started the search with the most recent memory first. Finally, a third strategy post-processed all memories using pruning, abstraction, and cueing. Pruning removed memories, abstraction used categories to reduce metric information, and the cueing process provided pointers for the subsequent recognition of episodes. We found that post-processing memories as an unconscious process was the most efficient strategy. This computational implementation provides a justification for the post-processing of memories as an efficient means of memory retrieval.
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