User Experience Sensor for Man–Machine Interaction Modeled as an Analogy to the Tower of Hanoi

Abstract

This paper presents a novel user experience optimization concept and method, named User Experience Sensor, applied within the Hybrid Intelligence System (HINT). The HINT system, defined as a combination of an extensive AI system and the possibility of attaching a human expert, is designed to be used by relational agents, which may have a physical form, such as a robot, a kiosk, be embodied in an avatar, or may also exist as only software. The proposed method focuses on automatic process evaluation as a common sensor for optimization of the user experience for every process stage and the indicator for human-expert automatic session activation. This functionality is realized by the User Experience Sensor, which constitutes one of main elements of the self-optimizing interaction system. The authors present the optimization mechanism of the HINT system as an analogy to the process of building a Tower of Hanoi. The proposed sensor evaluates the user experience and measures the user/employee efficiency at every stage of a given process, offering the user to choose other forms of information, interaction, or expert support. The designed HINT system is able to learn and self-optimize, making the entire process more intuitive and easy for each and every user individually. The HINT system with the proposed sensor, implemented in a window assembly facility, successfully reduced assembly time, increased employees’ satisfaction, and assembly quality. The proposed approach can be implemented in numerous man–machine interaction applications.

Implementation of a Hybrid Intelligence System Enabling the Effectiveness Assessment of Interaction Channels Use in HMI

“…This 'training quality' can be perceived as a result of (1) the training/teaching process (initial basic training as well as the mastering performed later) and (2) the personal predispositions of a particular employee, play an essential role in the decision-making process of the HINT system algorithm relaying especially on sensing capabilities of the IQS. The production line's sensors provide the knowledge of the apprentice's work quality [64] and enable adjusting the steering parameters for HMI guidance delivery channels, conceivably due to the HINT system's capability of full integration into the factory processing event bus. Based on the available information, the HINT system is designed to be able to distinguish alternate information channel scenarios and to utilize a control algorithm optimized with regard to a particular apprentice and the situation.…”

“…The IQS delivers the necessary data by comparing patterns of behavior saved in the set of basic instructions against the behavior of the worker and expected quality in advancements of current activity. The optimization process of selection of the best-performing channel to the situation conditions was already briefly introduced in [64], and presented by comparing it to the concept of Hanoi Towers. Meanwhile, the study presented in this article refers to the real-world conditions of a local furniture factory and the conclusions drawn from real-world use-case implementation and integration with an existing quality control system, by enhancing the functionality of an existing apprentice workstation with HMI instruments, and upgrading it to a quality-and process-related research station.…”

“…The presented implementation uses the algorithm described in [64], designed as a dual mode sensor including User Experience (UX) sensor and Interaction Quality Sensor (IQS). The idea and theoretical basis of this sensor are described as an analogy to the towers of Hanoi (see Figure 2).…”

“…The Hybrid Intelligence System (HINT) was first introduced theoretically in [64], where the authors presented a novel user experience optimization concept and method, named the User Experience Sensor. The concept presented there enabled the selection of the most effective channels of information transfer [73] available to be chosen from for any given stage: Choosing and switching between available information channels (forms of information for every given stage of window assembly) helps to optimize the efficiency of a production line (for a given employee) or of training of a particular new employee.…”

“…In the case of a classic approach (employee training without involving advanced IT systems) to the management of the employee training process, the evaluation (assessing the effectiveness of the teaching-learning process) and the prediction of their achieved proficiency level are difficult to implement (mostly due to the lack of appropriate procedures implementations, as well as reliable data) [62]. Nevertheless, new technologies based on Machine Learning [57], predictive analytics [63], and Big Data [56] can significantly improve employee onboarding processes within an organization [64,65]. Moreover, they can also provide insight into the KPIs (Key Performance Indicators) of the implementation process [66], which allows for systematic standardization of methods and teaching materials used in training [67], and for adapting the information transfer process to the individual predispositions of a particular individual (e.g., predominance of visual-or speech-based form of information, the speed of information transfer, etc.)…”