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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.
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Forget About It: Entity-Level Working Memory Models for Referring Expression Generation in Robot Cognitive Architectures
Working Memory (WM) plays a key role in natural language understanding and generation. To enable a human-like breadth and flexibility of language understanding and generation capabilities, cognitive systems for language-capable robots should feature a human-like WM system in a similarly central role. However, it is still quite unclear how robotic WM should be designed, as a variety of models of human WM have been proposed in cognitive psychology. Moreover, human reliance on WM during language production is sometimes to help the speaker rather than to help hearers. Thus, it is unclear whether different robotic WM systems might harm certain dimensions of interaction for the sake of the robot speaker’s ostensible ease of cognitive processing. In this paper we demonstrate how different models of human WM can be implemented into robot cognitive architectures. Our results suggest that these models can be effective in terms of accuracy, perceived naturalness, and perceived human-likeness.
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- Award ID(s):
- 2044865
- PAR ID:
- 10458325
- Date Published:
- Journal Name:
- Annual Meeting of the Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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