Unpacking the Role of Artificial Intelligence for a Multimodal Service System Design

Abstract

Since requirements of service demands are becoming increasingly complex and diversified, one of the success factors of a multimodal service system is its capability to design a specific service instance satisfying a specific set of requirements. This capability is further highlighted in Ad Hoc Multimodal Service Systems (AHMSSs), where service instances rarely follow a standard form of service delivery and exist only for a limited time. However, due to the increasing scale and frequency of services in many business and public sectors, meeting the desired level of capability has become troublesome. A well-designed Artificial Intelligence (AI) approach can be a solution to the difficulty by addressing the underlying complexity and uncertainty of the AHMSS design process. To conceptualize and foster AI applications to an AHMSS, this study identifies key decision-making problems in the AHMSS design process and discusses the role of AI in the process. The results will form the basis for AI development and implementation for an AHMSS and relevant service systems.

Advances in Explainable Artificial Intelligence and Edge Computing Applications

“…Prediction and decision-making models are also commonly used within AI and XAI models [5][6][7][8]. These models can be applied to different areas, such as tourist preferences, as proposed by Meira et al [6], that explore Machine Learning to predict users' ratings.…”

“…In this study, the CRISP-DM methodology is followed to analyze Machine Learning models that detect violence in video streams to compare their performance. Sung et al [8] also research the factors a multimodal service system has to achieve to satisfy a specific set of requirements. The study identifies key decision-making problems in the design process and analyzes the role played by Artificial Intelligence.…”