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Title: Handling Working Memory Knowledge Through a Consultant-Level Resource Management Strategy
Working memory is an important component of cognition that infuences key cognitive processes, such as language. As such, working memory should play a key role in cognitive models for languagecapable robots. The ways in which working memory bufers are organized within a robot’s architecture can inform processes such as Referring Expression Generation. Thus, it is important to understand how information and resources within working memory may be organized to lead to human-like robotic language. Previous work on the DIARC cognitive architecture described an entitylevel, feature-based working memory framework in which each known entity had its own dedicated working memory bufer. This paper expands on that framework and proposes a new resource management strategy in which sets of entities that belong to the same type share a single working memory bufer.We end the paper with a brief discussion of how this novel strategy compares to the previously implemented entity-level strategy.  more » « less
Award ID(s):
2044865
PAR ID:
10563662
Author(s) / Creator(s):
;
Publisher / Repository:
ACM
Date Published:
ISBN:
9798400703232
Page Range / eLocation ID:
999 to 1002
Format(s):
Medium: X
Location:
Boulder CO USA
Sponsoring Org:
National Science Foundation
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