Competence Awareness for Humans and Machines: A Survey and Future Research Directions from Psychology

被引：0

作者：

Kasmarik, Kathryn ^{[1
]}

Khani, Mahtab ^{[1
]}

Abpeikar, Shadi ^{[2
]}

Barlow, Michael ^{[1
]}

Carter, Olivia ^{[3
]}

Irish, Muireann ^{[4
]}

机构：

[1] Univ New South Wales, Sch Syst & Comp, Canberra, ACT, Australia

[2] Univ New South Wales, Engn & Technol, Canberra, ACT, Australia

[3] Univ Melbourne, Psychol Sci, Melbourne, Vic, Australia

[4] Univ Sydney, Psychol & Brain & Mind Ctr, Sydney, NSW, Australia

来源：

ACM COMPUTING SURVEYS | 2025年 / 57卷 / 01期

基金：

澳大利亚研究理事会;

关键词：

Competence aware; psychology; machine learning; INTRINSIC MOTIVATION; NEURAL-NETWORKS; EXPLORATION; FRAMEWORK; ROBOTICS; BEHAVIOR; SYSTEMS; AGENTS;

D O I：

10.1145/3689626

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Machine learning researchers are beginning to understand the need for machines to be able to self-assess their competence and express it in a human understandable form. However, current machine learning algorithms do not yet have the range or complexity of competence awareness measures present in humans. This review first describes progress towards competence awareness in machines, and then examines the psychology literature on competence awareness and competence motivation to identify the limitations of current competence awareness algorithms. The article concludes with a discussion of the necessary and promising future research directions for creating competence-aware machines.

引用

页数：26

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