• Corpus ID: 11725225

Performance evaluation of declarative memory systems in Soar

@inproceedings{Laird2011PerformanceEO,
  title={Performance evaluation of declarative memory systems in Soar},
  author={John E. Laird and Nate Derbinsky and Jonathan Voigt},
  year={2011}
}
A rarely studied issue with using persistent computational models is whether the underlying computational mechanisms scale as knowledge is accumulated through learning. In this paper we evaluate the declarative memories of Soar: working memory, semantic memory, and episodic memory, using a detailed simulation of a mobile robot running for one hour of real-time. Our results indicate that our implementation is sufficient for tasks of this length. Moreover our system executes orders of magnitudes… 
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This paper reviews the implementation of episodic memory in Soar and presents an expansive evaluation of that system, demonstrating useful applications of episodi memory across a variety of domains, including games, mobile robotics, planning, and linguistics.
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This paper evaluates one approach to forgetting knowledge that is not in active use and can likely be reconstructed if it becomes relevant, and applies this model for selective retention of learned knowledge to the working and procedural memories of Soar.
Extending Semantic and Episodic Memory to Support Robust Decision Making
  • J. Laird
  • Computer Science, Psychology
  • 2013
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For episodic memory, research has led to significant improvements in the efficiency of storage and retrieval through the exploitation of temporal contiguity, structural regularity, high cue structural selectivity, high temporal selectivity and low cue feature co-occurrence.
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Research in mobile robotics, relational and continuous model learning, and learning by situated, interactive instruction is presented, which improves Soar abilities for control of robots.
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The goal of my research is to develop and evaluate long-term declarative memory mechanisms that are effective and efficient across a variety of tasks.
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