Grounding From an AI and Cognitive Science Lens

被引：2

作者：

Bajaj, Goonmeet ^{[1
]}

Shalin, Valerie L. ^{[2
]}

Parthasarathy, Srinivasan ^{[3
]}

Sheth, Amit ^{[4
]}

机构：

[1] Ohio State Univ, Columbus, OH 43210 USA

[2] Wright State Univ, Dayton, OH 45435 USA

[3] Ohio State Univ, Data Analyt program, Columbus, OH 43210 USA

[4] Univ South Carolina, Comp Sci & Engn, Columbia, SC 29208 USA

来源：

IEEE INTELLIGENT SYSTEMS | 2024年 / 39卷 / 02期

基金：

美国国家科学基金会;

关键词：

Grounding; Collaboration; Machine learning; Cognitive science; Task analysis; Intelligent systems; Artificial intelligence; Neural engineering; Symbols;

D O I：

10.1109/MIS.2024.3366669

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neurosymbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.

引用

页码：66 / 71

页数：6

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