Multi-agent Systems for Personalized QoE-Management

Najjar, Amro; Serpaggi, Xavier; Gravier, Christophe; Boissier, Olivier

doi:10.1109/itc-28.2016.308

Cited by 8 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these reasons, as has been discussed by recent works on user modeling, intelligent agents have the potential to model user satisfaction, expectations, and seek to maximize the user QoE [31]. In these works, each user is represented by a personal agent that takes into account her current context and preferences and acts on her behalf [32].…”

Section: Quality Of Experience (Qoe)mentioning

confidence: 99%

Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Calvaresi

Calbimonte

Debusson

et al. 2019

IEEE/WIC/ACM International Conference on Web Intelligence

Self Cite

View full text Add to dashboard Cite

Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.

show abstract

Section: Quality Of Experience (Qoe)mentioning

confidence: 99%

Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Calvaresi

Calbimonte

Debusson

et al. 2019

IEEE/WIC/ACM International Conference on Web Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…So, in the future, we need to combine complex social networks with mature modeling concepts of agents, that is, large-scale simulation, to solve the current problem of low efficiency. In addition, it is necessary to use Repast for high performance computing (Repast HPC) [30,31], combining parallel computing technology and multi-agent modeling theory. Therefore, an agent-oriented parallel computing underlying framework is constructed, and functional modules required by users are also established.…”

Section: Discussionmentioning

confidence: 99%

Multi-Agent Simulation of Safe Livability and Sustainable Development in Cities

Pan

Yang

et al. 2020

Sustainability

View full text Add to dashboard Cite

Urban livability is an important factor affecting the sustainable development of modern cities. Safe livability is an important part of urban livability. In view of this, this paper takes security as an example, and based on the actual data of Futian District, Shenzhen City, China, establishes a multi-agent simulation model of urban safe livability. The dynamic interaction feedback mechanism between decision-making behaviors of residents and urban safe livability under the influence of environment and policies has been explored, and residents' decision-making simulation of the change of urban safe livability is realized. Finally, the main factors influencing urban safe livability are summarized through simulation conclusions. The research can not only provide scientific suggestions for improving the safe livability of Shenzhen, it also provides strong support for the sustainable development of the city.Sustainability 2020, 12, 2070 2 of 15 detector model. Liu and co-workers explored the complex interdependence between emerging tourism and the livability of Chinese cities. Based on the conceptual model and statistical analysis of 35 large-and medium-sized cities in China from 2003 to 2012, the potential threat of overdevelopment of tourism to the livability of the city was deduced [4]. Paul and colleagues took Kolkata, India as the research object, and based their study on integrated urban geographic factors (IUGFs), evaluating the changes to livability in the city center to understand the impact of changes in livability on urban development [5]. Khorasani and Zarghamfard [6] analyzed the influence of spatial factors at local and regional scales on livability, using the urban village of Walamin as an example. The results showed that there was a significant relationship between village spatial factors and livability indicators. Furlan and Petruccioli [7] proposed a model called transit oriented development (TOD). This model improves urban livability through better integration of urban transportation and land use strategies, thereby maintaining sustainable urbanization. Most of the studies in the above literature only consider the meaning and measurement indicators of a more comprehensive livable city. They are limited to the concept and interpretation of the concept. They do not consider changes in the livability of the city under the influence of various factors. They only focus on static spatial equilibrium and the estimation of urban livability under quantitative indicators. Moreover, they ignore complex human factors and individual decision-making behaviors, and it is difficult to reflect the complexity of urban spatial systems. Therefore, in our study, computer technology is used to study the interaction between many environmental factors and complex human factors. Moreover, computer technology is of great significance to the study of urban safe livability.Multi-agent modeling [8] refers to abstracting the basic elements of complex systems into agents. Then, an agent simulation model corresponding to the real...

show abstract

“…In particular, QoE comes handy when modeling users' satisfaction, expectations, and the will to maximize their objective with intelligent agents [26]. Each user can be bounded with a personal agent representing his/her context and preferences and acts on his/her behalf [27].…”

Section: Quality Of Experiencementioning

confidence: 99%

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

et al. 2021

View full text Add to dashboard Cite

Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.

show abstract

Multi-agent Systems for Personalized QoE-Management

Cited by 8 publications

References 22 publications

Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Social Network Chatbots for Smoking Cessation: Agent and Multi-Agent Frameworks

Multi-Agent Simulation of Safe Livability and Sustainable Development in Cities

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

Contact Info

Product

Resources

About