Human-centric Requirements Engineering for Artificial Intelligence Software Systems

Ahmad, Khlood

doi:10.1109/re51729.2021.00070

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Strengths, weaknesses, and challenges facing these applications were outlined. In Artificial Intelligence (AI) based systems, the current literature has focused on using AI to manage RE activities with limited research on RE for AI [20,21,22]. This domain includes mainly AI and machine learning components.…”

Section: Related Workmentioning

confidence: 99%

“…This domain includes mainly AI and machine learning components. Challenges and limitations of existing RE for AI were identified, and research perspectives and industrial guidelines were recommended [20,21,22]. This domain includes mainly AI and machine learning components.…”

Section: Related Workmentioning

confidence: 99%

“…Requirements Engineering (RE) is being increasingly applied in different business domains, and its application benefits are evaluated as valuable [2,3,4,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Despite this active RE application and its added value in different domains, the TLC domain has not yet been approached, regardless of its industrial interest.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2024

IJNSA

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Requirements Engineering (RE) is the most important activity and pivot phase in the software development life cycle. It consists mainly of requirements analysis, design, and specification. The application of RE in a business domain aims to generalize its different specific approaches by meta-models under which each specific approach is an instance or specialization. RE is being applied more and more in different business domains, and its application benefits are found to be valuable. Despite these active RE applications and their added values in different domains, the Traffic Light Control (TLC) domain has not yet been approached, regardless of its everyday interest. The work presented in this paper is part of a TLC enhancement project that explores RE's potential contributions to leveraging TLC quality. Thus, in this work, different recent specific approaches to TLC are analyzed, and a large part of this business domain's functional/ non-functional requirements are elicited and specified. Moreover, the traffic context-aware traffic light control systems (CTLC) are also considered. They are deeply analyzed and compared to the typical TLC systems. As a primary result, the tangling of different concerns was stated, and a separate concerns paradigm was applied. This leads to requirements specification through separated agents. This should lead to TLC and CTLC systems development and maintenance cost reduction. An important agent dealing with security aspects and their evolving technologies is introduced. The impact evaluations of the RE on some TLC and CTLC current approaches are also presented.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Untitled

2024

IJNSA

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“…User-centric artificial intelligence (AI) has emerged as a viable strategy for developing intelligent systems that can better fulfill users' demands and preferences [1][2][3]. In contrast to traditional AI systems, which are designed to optimize a certain purpose or task, user-centric AI systems are concerned with offering tailored services that are suited to each user's distinctive traits and aspirations.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

et al. 2023

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Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results.

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Requirements Engineering for Artificial Intelligence: What Is a Requirements Specification for an Artificial Intelligence?

Berry

2022

Lecture Notes in Computer Science

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Human-centric Requirements Engineering for Artificial Intelligence Software Systems

Cited by 5 publications

References 21 publications

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Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

Requirements Engineering for Artificial Intelligence: What Is a Requirements Specification for an Artificial Intelligence?

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