Intelligent Agents as Trusting Teammates: Enhancing Efficiency and Collaboration in Labor-Intensive Tasks

Dr. Adeola, Akinyemi and Prof. Chinedu, Okafor and Dr. Fatima, Yusuf (2023) Intelligent Agents as Trusting Teammates: Enhancing Efficiency and Collaboration in Labor-Intensive Tasks. Journal of Science, Research and Teaching, 2 (4). pp. 228-235. ISSN 2181-4406

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Abstract

The integration of intelligent agents into human-machine teams (HMTs) is transforming the execution of labor-intensive tasks across various industries by enhancing efficiency and collaboration. This review article explores the role of intelligent agents as trusting teammates, focusing on their ability to improve task performance and team dynamics. The establishment of trust between human and machine team members is identified as a critical factor for successful collaboration. Key metrics for evaluating HMT performance, as highlighted by Damacharla et al. (2020), include reliability, transparency, and shared mental models. The article discusses the impact of voice-based synthetic assistants on emergency care providers, demonstrating significant improvements in training efficacy and performance (Damacharla et al., 2018). Additionally, the novel human-in-the-loop (HIL) simulation method proposed by Damacharla et al. (2020) is examined as a means to standardize and optimize HMTs. Despite the promising benefits, several challenges remain, including ethical considerations, skill complementarity, adaptability, and human factors. Future research directions emphasize the development of personalized AI teammates, cross-domain collaboration, long-term studies, and advanced trust-building mechanisms. By addressing these challenges and leveraging the strengths of both human expertise and machine capabilities, HMTs have the potential to revolutionize labor-intensive tasks and improve overall productivity and outcomes. This article underscores the importance of standardized evaluation methods, ethical guidelines, and adaptive AI technologies in shaping the future of human-machine collaboration.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	07 Aug 2024 10:41
Last Modified:	14 Aug 2024 09:58
URI:	http://eprints.umsida.ac.id/id/eprint/13954

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