by Berk Buzcu, Vanitha Varadhajaran, Igor Tchappi, Amro Najjar, Davide Calvaresi and Reyhan Aydoğan
Abstract People’s awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems.
by Giovanni Ciatto, Matteo Magnini, Berk Buzcu, Reyhan Aydoğan, and Andrea Omicini
Abstract Building on prior works on explanation negotiation protocols, this paper proposes a general-purpose protocol for multi-agent systems where recommender agents may need to provide explanations for their recommendations. The protocol specifies the roles and responsibilities of the explainee and the explainer agent and the types of information that should be exchanged between them to ensure a clear and effective explanation.
by Mehmet Onur Keskin, Berk Buzcu, and Reyhan Aydoğan
Abstract Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers.
by Berk Buzcu, Melissa Tessa, Igor Tchappi, Amro Najjar, Joris Hulstijn, Davide Calvaresi and Reyhan Aydoğan
Abstract The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human–machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms.
by Berk Buzcu, Kuru Emre, and Reyhan Aydoğan
Abstract With the pervasive usage of recommendation systems across various domains, there is a growing need for transparent and convincing interactions to build a rapport with the system users. Incorporating explainability into recommendation systems has become a promising strategy to bolster user trust and sociability. This study centers on recommendation systems that leverage varying explainability techniques to cultivate trust by delivering comprehensible customized explanations for the given recommendations.
by Berk Buzcu, Emre Kuru, Davide Calvaresi and Reyhan Aydoğan
Abstract As recommendation systems become increasingly prevalent in numerous fields, the need for clear and persuasive interactions with users is rising. Integrating explainability into these systems is emerging as an effective approach to enhance user trust and sociability. This research focuses on recommendation systems that utilize a range of explainability techniques to foster trust by providing understandable personalized explanations for the recommendations made.
by Berk Buzcu, Yvan Pannatier, Reyhan Aydoğan, Michael I. Schumacher, Jean-Paul Calbimonte, and Davide Calvaresi
Abstract Existing agent-based chatbot frameworks need seamless mechanisms to include explainable dialogic engines within the contextual flow. To this end, this paper presents a set of novel modules within the EREBOTS agent-based framework for chatbot development, including dialog-based plug-and-play custom algorithms, agnostic back/front ends, and embedded interactive explainable engines that can manage human feedback at run time.
