###### Abstract  
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; congestion; reinforcement learning; time management; game theory; personalization and recommendation  
visitors' QoE. The way QoE is formed was studied in Reference [3] where the authors examined the impact of visitor expectations and experiences on their perceived QoE. Towards studying this impact, the authors consider the relationship between the cognitive opinions of visitors (i.e., perceived quality and/or disconfirmation) and their affective opinion (i.e., positive emotions). QoE has also been studied in relation to five main directions, i.e., easiness and fun, cultural entertainment, personal identification, historical reminiscences, and escapism, in Reference [4] via a questionnaire-based study with real museum visitors. In Reference [5], the authors have examined the impact of providing story-centric narrations with references to the exhibits on visitors' QoE and engagement to the museum. This work focuses on leveraging the advantages of mobile-based guided tours, which are either audio/multimedia guides that visitors can rent or borrow when in the museum or applications that visitors can load onto their own smartphones and tablets. Moreover, in Reference [6], the authors aim at establishing the link between tourists' perceptions on cultural offers and their overall satisfaction while exploiting the impact of visitors' negative experiences on the museum's word-of-mouth reputation. In Reference [7], the authors have developed a visualization tool to monitor visitor navigation within the museum and extract useful information regarding their interests and their visiting style, which can be further exploited to form their QoE-related parameters. [PERSON] in Reference [8] also shows that the QoE of a museum visitor depends on personalized factors like the visitor's style as well as on museum-related factors, e.g., museum congestion caused by the number of visitors that are present. Recent studies have correlated visitor's QoE when touring in the museum. In References [9; 10], the authors propose a museum touring framework that aims at minimizing the visiting time of a group of visitors, following a mixed integer programming approach. However, this framework is agnostic with respect to visiting styles. A personalized approach to the previous problem is proposed in Reference [11] where the proposed museum touring solution considers both the preferences of visitors and the prestige of exhibits while maximizing the QoE expressed as their total interest towards a set of exhibits.