Quality of Experience in Cyber-Physical Social Systems: A Cultural Heritage Space Use Case

In this PhD thesis, the focus is placed on the optimization of user Quality of Experience (QoE) in Cyber Physical Social Systems and speci cally in cultural heritage spaces. In order to achieve maximization of visitor perceived satisfaction, the challenges associated with visitor optimal decision making regarding touring choices and strategies in a museum or a cultural heritage space are examined and the problem of museum congestion is αddressed. Cultural heritage spaces, and museums in particular, constitute a special type of socio-physical system because, in contrast to other social systems like schools or churches, user experience is primarily controlled by the visitors themselves. Such a system also embodies both human behaviors and physical and technical constraints, a fact that makes adopting a socio-technical perspective in order to improve the visiting experience, essential. Within the above setting, quantitative models and functions are initially formulated to express the visitor experience that is gained throughout a touring process. The functions are based on several socio-physical and behavioral factors. Using this QoE modeling approach, the problem of how to optimise visitor route choices is addressed. A social recommendation and personalization framework is also presented that exploits common visitor characteristics and recommends a set of exhibits to be visited. The creation of self-organizing museum visitor communities are proposed as a means to enhance the visiting experience. They exploit visitor personal characteristics and social interactions and are based on a participatory action research (PAR) process. Recommendation Selection and Visiting Time Management (RSVTM) are combined and formulated into a two-stage distributed algorithm, based on game theory and reinforcement learning. In addition, this PhD thesis examines the problem of congestion management in cultural heritage spaces from a more pragmatic perspective, considering visitor behavioral characteristics and risk preferences. The motivation behind this approach arose from the observation that, in cultural heritage spaces, people interact with each other and consequently the decisions and behavior of one visitor influence and are influenced by others. It is, therefore, important to understand the unknown behavior tendencies of visitors especially when making decisions in order to improve their visiting experience and reduce museum congestion. The proposed mechanisms are founded on and powered by the principles of Prospect Theory and the Tragedy of the Commons. Particular attention is paid to modeling and capturing visitor behaviors and decision making under the potential risks and uncertainties which are typically encountered by visitors during their visit. According to their relative popularity and attractiveness, exhibits at a cultural heritage site are classi ed into two main categories: safe exhibits and Common Pool of Resources (CPR) exhibits. CPR exhibits are considered non-excludable and rivalrous in nature, meaning that they may experience "failure" due to over-exploitation. As a result, a visitor's decision to invest time at a CPR exhibit is regarded as risky because his/her perceived satisfaction greatly depends on the cumulative time spent at it by all visitors. A non-cooperative game among the visitors is formulated and solved in a distributed manner in order to determine the optimal investment time at exhibits for each visitor, while maximizing the visitor's perceived satisfaction. Detailed numerical results are presented, which provide useful insights into visitor behaviors and how these influence visitor perceived satisfaction, as well as museum congestion. Finally, pricing is introduced as an effective mechanism to address the problem of museum congestion. Motivated by several studies that position pricing as a mechanism to prevent overcrowding in museums, this thesis analyzes and studies the impact of different pricing policies on visitor decisions when they act as prospect-theoretic decision-makers. The theory of S-modular games is adopted to determine the time invested by each visitor at exhibits while maximizing satisfaction gained

Ποιότητα εμπειρίας χρήστη σε κυβερνο-φυσικά κοινωνικά συστήματα: το παράδειγμα των χώρων πολιτισμικής κληρονομιάς

In this PhD thesis, the focus is placed on the optimization of user Quality of Experience (QoE) in Cyber Physical Social Systems and specifically in cultural heritage spaces. In order to achieve maximization of visitor perceived satisfaction, the challenges associated with visitor optimal decision making regarding touring choices and strategies in a museum or a cultural heritage space are examined and the problem of museum congestion is addressed. Cultural heritage spaces, and museums in particular, constitute a special type of socio-physical system because, in contrast to other social systems like schools or churches, user experience is primarily controlled by the visitors themselves. Such a system also embodies both human behaviors and physical and technical constraints, a fact that makes adopting a socio-technical perspective in order to improve the visiting experience, essential. Within the above setting, quantitative models and functions are initially formulated to express the visitor experience that is gained throughout a touring process. The functions are based on several socio-physical and behavioral factors. Using this QoE modeling approach, the problem of how to optimise visitor route choices is addressed. A social recommendation and personalization framework is also presented that exploits common visitor characteristics and recommends a set of exhibits to be visited. The creation of self-organizing museum visitor communities are proposed as a means to enhance the visiting experience. They exploit visitor personal characteristics and social interactions and are based on a participatory action research (PAR) process. Recommendation Selection and Visiting Time Management (RSVTM) are combined and formulated into a two-stage distributed algorithm, based on game theory and reinforcement learning. In addition, this PhD thesis examines the problem of congestion management in cultural heritage spaces from a more pragmatic perspective, considering visitor behavioral characteristics and risk preferences. The motivation behind this approach arose from the observation that, in cultural heritage spaces, people interact with each other and consequently the decisions and behavior of one visitor influence and are influenced by others. It is, therefore, important to understand the unknown behavior tendencies of visitors especially when making decisions in order to improve their visiting experience and reduce museum congestion. The proposed mechanisms are founded on and powered by the principles of Prospect Theory and the Tragedy of the Commons. Particular attention is paid to modeling and capturing visitor behaviors and decision making under the potential risks and uncertainties which are typically encountered by visitors during their visit. According to their relative popularity and attractivenss, exhibits at a cultural heritage site are classified into two main categories: safe exhibits and Common Pool of Resources (CPR) exhibits. CPR exhibits are considered non-excludable and rivalrous in nature, meaning that they may experience "failure" due to over-exploitation. As a result, a visitor's decision to invest time at a CPR exhibit is regarded as risky because his/her perceived satisfaction greatly depends on the cumulative time spent at it by all visitors. A non-cooperative game among the visitors is formulated and solved in a distributed manner in order to determine the optimal investment time at exhibits for each visitor, while maximizing the visitor's perceived satisfaction. Detailed numerical results are presented, which provide useful insights into visitor behaviors and how these influence visitor perceived satisfaction, as well as museum congestion.Finally, pricing is introduced as an effective mechanism to address the problem of museum congestion. Motivated by several studies that position pricing as a mechanism to prevent overcrowding in museums, this thesis analyzes and studies the impact of different pricing policies on visitor decisions when they act as prospect-theoretic decision-makers. The theory of S-modular games is adopted to determine the time invested by each visitor at exhibits while maximizing satisfaction gained.Σε αυτή τη διδακτορική διατριβή, η έμφαση δίνεται στη βελτιστοποίηση της ποιότητας της εμπειρίας του χρήστη σε Κυβερνοφυσικά Κοινωνικά Συστήματα και ειδικότερα σε χώρους πολιτιστικής κληρονομιάς. Εξετάζονται θέματα που αφορούν τις βέλτιστες αποφάσεις των επισκεπτών σχετικά με τις επιλογές και τις στρατηγικές περιήγησής τους μέσα σε ένα μουσείο ή χώρο πολιτισμού, με απώτερο στόχο τη μεγιστοποίηση της ευχαρίστησης των επισκεπτών και την αντιμετώπιση του προβλήματος της συμφόρησης των μουσείων. Οι χώροι πολιτιστικής κληρονομιάς και ειδικότερα τα μουσεία αποτελούν έναν ειδικό τύπο κοινωνικό-φυσικών συστημάτων γιατί σε αντίθεση με άλλα κοινωνικά συστήματα, όπως είναι τα σχολεία ή οι εκκλησίες, η εμπειρία του χρήστη εξαρτάται κυρίως από τον ίδιο. Επίσης οι χώροι πολιτιστικής κληρονομιάς εμπεριέχουν όχι μόνο φυσικούς και τεχνικούς περιορισμούς αλλά και περιορισμούς που προέρχονται από ανθρώπινες συμπεριφορές. Η υιοθέτηση λοιπόν μίας κοινωνικό-τεχνικής αντίληψης με στόχο τη βελτιστοποίηση της εμπειρίας του επισκέπτη κρίνεται απαραίτητη και υιοθετείται στην παρούσα διδακτορική έρευνα. Μέσα σε αυτό το πλαίσιο, αρχικά διατυπώνονται ποσοτικά μοντέλα και συναρτήσεις, που βασίζονται σε διάφορους κοινωνικό-φυσικούς παράγοντες και εκφράζουν την εμπειρία του επισκέπτη κατά τη διάρκεια της επίσκεψής του σε ένα μουσείο. Χρησιμοποιώντας τα μοντέλα και τις συναρτήσεις αυτές, μελετώνται πως μπορούν να βελτιωθούν οι επιλογές περιήγησης του επισκέπτη μέσα σε ένα μουσείο. Επιπροσθέτως, παρουσιάζεται ένας μηχανισμός εξατομικευμένων κοινωνικών προτάσεων, που αφενός βρίσκει τα κοινά χαρακτηριστικά των επισκεπτών και αφετέρου προτείνει στους επισκέπτες ένα υποσύνολο εκθεμάτων για να επισκεφτούν. Ακόμα παρουσιάζεται η δημιουργία αυτο-οργανούμενων ομάδων επισκεπτών, οι οποίες εκμεταλλεύονται προσωπικά χαρακτηριστικά των επισκεπτών καθώς και τυχόν μεταξύ τους κοινωνικές αλληλεπιδράσεις με στόχο τη βελτίωση της μουσειακής εμπειρίας τους. Επίσης διατυπώνεται το συνδυαστικό πρόβλημα της επιλογής μίας προτεινόμενης περιήγησης από το μουσείο και του καθορισμού του χρόνου επίσκεψής στο μουσείο. Για την επίλυση του προβλήματος αυτού, χρησιμοποιείται ένας κατανεμημένος διεπίπεδος αλγόριθμος που βασίζεται στη Θεωρία Παιγνίων και στην ενισχυμένη μάθηση. Παράλληλα, η διδακτορική αυτή διατριβή ερευνά τη διαχείριση της συμφόρησης των μουσείων από μία πιο ρεαλιστική οπτική, που λαμβάνει υπόψιν της χαρακτηριστικά της συμπεριφοράς των επισκεπτών καθώς και τις προτιμήσεις τους όσον αφορά το ρίσκο. Το κίνητρο αυτής της προσέγγισης προέρχεται από την παρατήρηση ότι στους χώρους πολιτιστικής κληρονομιάς, οι άνθρωποι αλληλοεπιδρούν μεταξύ τους και συνεπώς η συμπεριφορά και οι αποφάσεις ενός επισκέπτη επηρεάζουν και επηρεάζονται από τους άλλους. Προκειμένου να βελτιωθεί η εμπειρία των επισκεπτών και να μειωθεί η συμφόρηση των μουσείων, είναι απαραίτητο να κατανοηθούν οι άγνωστες συμπεριφορές των επισκεπτών - ειδικά όσον αφορά τις διαδικασίες λήψης αποφάσεων. Οι μηχανισμοί που προτείνονται βασίζονται στη Θεωρία Προοπτικής και στην Τραγωδία των Κοινών, και σχετίζονται με την αποτύπωση και μοντελοποίηση της συμπεριφοράς των επισκεπτών καθώς και τη λήψη αποφάσεων υπό συνθήκες ρίσκου ή αβεβαιότητας, οι οποίες και συναντώνται συχνά κατά τη διάρκεια μίας επίσκεψης σε ένα μουσείο. Η παρούσα έρευνα κατηγοριοποιεί τα εκθέματα ενός μουσείου σε δύο ομάδες: στα ασφαλή και στα «Κοινού Αποθέματος Πόρων» εκθέματα. Ο διαχωρισμός αυτός γίνεται με βάση το πόσο δημοφιλή και ελκυστικά έργα τέχνης θεωρούνται από τους επισκέπτες του μουσείου. Τα «Κοινού Αποθέματος Πόρων» εκθέματα θεωρούνται τα διάσημα έργα τέχνης, τα οποία λόγω υπερβολικού συνωστισμού γύρω τους, μπορεί να «καταρρεύσουν». Επομένως η απόφαση ενός επισκέπτη να αφιερώσει χρόνο σε ένα «Κοινού Αποθέματος Πόρων» έκθεμα θεωρείται ότι ενέχει ρίσκο γιατί η ευχαρίστηση που θα λάβει εξαρτάται σε πολύ μεγάλο βαθμό από το συνολικό χρόνο που θα αφιερώσουν σε αυτό και οι υπόλοιποι επισκέπτες. Ένα μη συνεργατικό παίγνιο ανάμεσα στους επισκέπτες εφαρμόζεται και επιλύεται για τον υπολογισμό του βέλτιστου χρόνου που πρέπει να αφιερώσει κάθε επισκέπτης στα εκθέματα ώστε να μεγιστοποιήσει την ευχαρίστησή του και να μην οδηγηθούν σε κατάρρευση τα εκθέματα. Λεπτομερή αριθμητικά αποτελέσματα παρουσιάζονται και δίνουν ενδιαφέρουσες και χρήσιμες πληροφορίες σχετικά με τη συμπεριφορά των επισκεπτών και το πως αυτή επηρεάζει τη λαμβανόμενη ευχαρίστησή τους αλλά και τη συμφόρηση των μουσείων. Η παρούσα διδακτορική έρευνα ολοκληρώνεται με την παρουσίαση διαφόρων μηχανισμών κοστολόγησης των επισκεπτών ως ένα μέτρο αντιμετώπισης της συμφόρησης των μουσείων. Τα αποτελέσματα που παρουσιάζονται επιβεβαιώνουν ότι κατάλληλοι μηχανισμοί τιμολόγησης μπορούν να οδηγήσουν ένα μουσείο σε μεγαλύτερη σταθερότητα, αφού μειώνουν την πιθανότητα κατάρρευσης των εκθεμάτων, χωρίς παράλληλα να επηρεάζουν αρνητικά την ποιότητα της εμπειρίας των επισκεπτών

Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

This paper addresses the problem of museum visitors’ Quality of Experience (QoE) optimization by viewing and treating the museum environment as a cyber-physical social system. To achieve this goal, we harness visitors’ internal ability to intelligently sense their environment and make choices that improve their QoE in terms of which the museum touring option is the best for them and how much time to spend on their visit. We model the museum setting as a distributed non-cooperative game where visitors selfishly maximize their own QoE. In this setting, we formulate the problem of Recommendation Selection and Visiting Time Management (RSVTM) and propose a two-stage distributed algorithm based on game theory and reinforcement learning, which learns from visitor behavior to make on-the-fly recommendation selections that maximize visitor QoE. The proposed framework enables autonomic visitor-centric management in a personalized manner and enables visitors themselves to decide on the best visiting strategies. Experimental results evaluating the performance of the proposed RSVTM algorithm under realistic simulation conditions indicate the high operational effectiveness and superior performance when compared to other recommendation approaches. Our results constitute a practical alternative for museums and exhibition spaces meant to enhance visitor QoE in a flexible, efficient, and cost-effective manner

Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

This paper addresses the problem of museum visitors’ Quality of Experience (QoE) optimization by viewing and treating the museum environment as a cyber-physical social system. To achieve this goal, we harness visitors’ internal ability to intelligently sense their environment and make choices that improve their QoE in terms of which the museum touring option is the best for them and how much time to spend on their visit. We model the museum setting as a distributed non-cooperative game where visitors selfishly maximize their own QoE. In this setting, we formulate the problem of Recommendation Selection and Visiting Time Management (RSVTM) and propose a two-stage distributed algorithm based on game theory and reinforcement learning, which learns from visitor behavior to make on-the-fly recommendation selections that maximize visitor QoE. The proposed framework enables autonomic visitor-centric management in a personalized manner and enables visitors themselves to decide on the best visiting strategies. Experimental results evaluating the performance of the proposed RSVTM algorithm under realistic simulation conditions indicate the high operational effectiveness and superior performance when compared to other recommendation approaches. Our results constitute a practical alternative for museums and exhibition spaces meant to enhance visitor QoE in a flexible, efficient, and cost-effective manner

An Enhanced Methodology for Creating Digital Twins within a Paleontological Museum Using Photogrammetry and Laser Scanning Techniques

In recent years, researchers in the field of natural heritage have intensified their efforts to develop new ways to enhance the promotion and accessibility of natural content in order to attract more audiences using virtual representations of physical objects (digital twins). Therefore, they are increasingly incorporating new technologies and digital tools in their operations since their usage by the general public and in the natural heritage (NH) museums is considered particularly effective. Simultaneously, the increasing quality of the produced digitizations has opened up new opportunities for the exploitation of the outcomes of digitization beyond the initial anticipations. Responding to the growing demand of museum visitors for a personalized digital tour experience, especially amidst the recent COVID-19 pandemic, the v-PalM project aims to develop a digital platform to offer virtual guidance and education services at the Museum of Paleontology and Geology, which is hosted at the National Kapodistrian University of Athens. The development of the platform will be based on collecting data through various methods, including crowdsourcing, innovative information, and communication technologies, taking advantage of content digitization using 3D scanning devices. This paper demonstrates an enhanced methodology for the digitization of paleontological exhibits. The methodology uses photogrammetry and laser scanning methods from various devices, such as drones, laser scanners, and smartphones. These methods create digital twins that are suitable for various scenarios, including research, education, and entertainment. The proposed methodology has been applied to over fifty paleontological museum exhibits of varying sizes and complexities, and the resulting 3D models exhibit high accuracy in both their material and geometric aspects, while they also feature crucial details that assist researchers and the scientific community