Browsing by Author "Buzcu, Berk"

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Open Access
Conflict-based negotiation strategy for human-agent negotiation
(Springer, 2023-12) Keskin, Mehmet Onur; Buzcu, Berk; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Keskin, Mehmet Onur; Buzcu, Berk
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. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.
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Explanation-based negotiation protocol for nutrition virtual coaching
(ACM, 2023) Buzcu, Berk; Varadhajaran, V.; Tchappi, I.; Najjar, A.; Calvaresi, D.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan
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. Enabling NVC to explain recommendations and discuss (argument) dietary solutions and alternative items or behaviors is crucial to improve the transparency of these applications and enhance user acceptability and retain their engagement. This study primarily focuses on virtual agents personalizing the generation of food recipes recommendation according to users’ allergies, eating habits, lifestyles, nutritional values, etc. Although the agent would nudge the user to consume healthier food, users may tend to object in favor of tastier food. To resolve this divergence, we propose a user-agent negotiation interacting over the revision of the recommendation (via feedback and explanations) or convincing (via explainable arguments) the user of its benefits and importance. Finally, the paper presents our initial findings on the acceptability and usability of such a system obtained via tests with real users. Our preliminary experimental results show that the majority of the participants appreciate the ability to express their feedback as well as receive explanations of the recommendations, while there is still room for improvement in the persuasiveness of the explanations.
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Fully autonomous trustworthy unmanned aerial vehicle teamwork: A research guideline using level 2 blockchain
(IEEE, 2023-02) Buzcu, Berk; Özgün, Mehmet Mert; Gürcan, Ö.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan
The vast range of possible fully autonomous multiunmanned aerial vehicle (multi-UAV) operations is creating a new and expanding market where technological advances are happening at a breakneck pace. The integration of UAVs in airspaces (not just for military purposes but also for civil, commercial, and leisure use) is essential in realizing the potential of this growing industry. Furthermore, with the advent of 6G, such integration will be cost-effective and more flexible. However, to reach widespread adoption, new models focusing on the safety, efficiency, reliability, and privacy of fully autonomous multi-UAV operations, ensuring that the operation history is trustworthy and can be audited by the relevant stakeholders, need to be developed. Accordingly, this work presents a research guideline for fully autonomous trustworthy UAV teamwork through layer 2 blockchains that provide efficient, privacy-preserving, reliable, and secure multi-UAV service delivery. We show the implications of this approach for an aerial surveillance use case.
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A general-purpose protocol for multi-agent based explanations
(Springer, 2023) Ciatto, G.; Magnini, M.; Buzcu, Berk; Aydoğan, Reyhan; Omicini, A.; Computer Science; AYDOĞAN, Reyhan; Buzcu, Berk
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. However, it does not prescribe any particular sort of recommendation or explanation, hence remaining agnostic w.r.t. such notions. Novelty lays in the extended support for both ordinary and contrastive explanations, as well as for the situation where no explanation is needed as none is requested by the explainee. Accordingly, we formally present and analyse the protocol, motivating its design and discussing its generality. We also discuss the reification of the protocol into a re-usable software library, namely PyXMas, which is meant to support developers willing to build explainable MAS leveraging our protocol. Finally, we discuss how custom notions of recommendation and explanation can be easily plugged into PyXMas.
Open Access
Towards interactive explanation-based nutrition virtual coaching systems
(Springer, 2024-01) Buzcu, Berk; Tessa, M.; Tchappi, I.; Najjar, A.; Hulstijn, J.; Calvaresi, D.; Aydoğan, Reyhan; Computer Science; AYDOĞAN, Reyhan; Buzcu, Berk
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. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.
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Towards transparent recommenders : an explanation-based negotiation approach
Buzcu, Berk; Aydoğan, Reyhan; Aydoğan, Reyhan; Kıraç, Mustafa Furkan; Aydemir, F. B.; Department of Computer Science
As more and more recommendation systems are used in different areas and they are exposed to more ethical concerns, there is a growing demand for transparent and persuasive interactions with these systems. Toward this end, incorporating explainability in recommendation systems has emerged as a promising approach to enhance sociability and user trust. This thesis focuses on recommendation systems that utilize explainability techniques to foster sociability by providing precise and understandable explanations for their recommendations. The proposed recommendation system utilizes a combination of data-driven transparent mechanisms and human-agent negotiation approaches. The system generates personalized recommendations based on individual preferences and other similar user-tailored factors and engages in a negotiation with the users via discussions through explanations and real-time feedback mechanisms. The system reacts to user responses online, tailoring subsequent recommendations and explanations to convince the user. This thesis encompasses Nutrition Virtual Coach (NVC) agents that generate personalized food recommendations based on individual factors like allergies, eating habits, lifestyles, and ingredient preferences. It mainly focuses on explanation generation techniques to enhance the transparency and trustworthiness of the system by improving the NVC agent's sociability in multiple steps. Ultimately, we incrementally conducted multiple experiments with participants from various backgrounds to evaluate the acceptability and effectiveness of the system. The findings from the experiments generally indicate that most participants appreciate the opportunity to provide feedback and receive explanations for the given recommendations. The participants prefer receiving information tailored to their specific needs and expectations. Additionally, the participants expressed their thoughts on various forms of explanations. The findings indicate that comparative explanations are not appreciated as much as informative explanations. The users seem to prefer direct and simple explanations that explain items respectively.