Author name cluster

Pınar Yolum

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

JAAMAS Journal 2026 Journal Article

From human teams to hybrid intelligence teams: identifying, characterizing, and evaluating foundational quality attributes

Davide Dell’Anna
Pradeep K. Murukannaiah
Pınar Yolum

Abstract Hybrid Intelligence (HI) is an emerging paradigm in which artificial intelligence (AI) augments human intelligence. The current literature lacks systematic models that guide the design and evaluation of HI systems. Further, discussions around HI primarily focus on technology, neglecting the holistic human-AI ensemble. In this paper, we take the initial steps toward the development of a quality model for characterizing and evaluating HI systems from a human-AI teams perspective. We first conducted a study investigating the adequacy of properties commonly associated with effective human teams to describe HI. The study features the insights of 50 HI researchers, and shows that various human team properties, including boundedness, interdependence, competency, purposefulness, initiative, normativity, and effectiveness, are important for HI systems. Based on these results, we developed a quality model for HI teams composed of seven high-level quality attributes, further refined into 16 specific ones. To evaluate the relevance and understanding of the proposed attributes, we conducted a second empirical investigation by staging competitions in which participants used the quality model to develop and analyze HI usage scenarios. Our analysis of 48 collected scenarios, which we openly release, confirms the proposed attributes’ relevance and highlights insights that emerge when designers consider the quality model in HI system design.

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IJCAI Conference 2025 Conference Paper

A Survey on One-To-Many Negotiation: A Taxonomy of Interdependency

Tamara C. P. Florijn
Pınar Yolum
Tim Baarslag

One-to-many negotiations are widely applied in various domains, contributing to efficient resource allocation and effective decision making. This wide variety of applications also brings a wide variety of implemented protocols, terminology and utility functions, which makes it hard to compare and improve strategies using existing solutions. We introduce a meta-model of negotiations, which characterizes almost all one-to-many negotiation research, bringing a unified description of the negotiations. This meta-model allows us to identify different classes of interdependency based on utility functions. We show how existing one-to-many negotiations are related to each other, finding new insights and identifying knowledge gaps. We suggest that a general utility function framework and benchmark scenarios for one-to-many negotiations could accommodate future advancement in this field.

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AAMAS Conference 2023 Conference Paper

Explain to Me: Towards Understanding Privacy Decisions

Gonul Ayci
Arzucan Özgür
Murat Şensoy
Pınar Yolum

Privacy assistants help users manage their privacy online. Their tasks could vary from detecting privacy violations to recommending sharing actions for content that the user intends to share. Recent work on these tasks are promising and show that privacy assistants can successfully tackle them. However, for such privacy assistants to be employed by users, it is important that these assistants can explain their decisions to users. Accordingly, this work develops a methodology to create explanations of privacy. The methodology is based on identifying important topics in a domain of interest, providing explanation schemes for decisions, and generating them automatically. We apply our proposed methodology on a real-world privacy data set, which contains images labeled as private or public to explain the labels.

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JAAMAS Journal 2020 Journal Article

Assisting humans in privacy management: an agent-based approach

A. Can Kurtan
Pınar Yolum

Abstract Image sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and specify a privacy setting for each image that they upload. This is difficult for two main reasons: first, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their privacy preferences, editing these privacy settings is cumbersome and requires too much effort, interfering with the quick sharing behavior expected on an online social network. Accordingly, this paper proposes a privacy recommendation model for images using tags and an agent that implements this, namely pelte. Each user agent makes use of the privacy settings that its user have set for previous images to predict automatically the privacy setting for an image that is uploaded to be shared. When in doubt, the agent analyzes the sharing behavior of other users in the user’s network to be able to recommend to its user about what should be considered as private. Contrary to existing approaches that assume all the images are available to a centralized model, pelte is compatible to distributed environments since each agent accesses only the privacy settings of the images that the agent owner has shared or those that have been shared with the user. Our simulations on a real-life dataset shows that pelte can accurately predict privacy settings even when a user has shared a few images with others, the images have only a few tags or the user’s friends have varying privacy preferences.

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JAAMAS Journal 2014 Journal Article

Dynamically generated commitment protocols in open systems

Akın Günay
Michael Winikoff
Pınar Yolum

Abstract Agent interaction is a fundamental part of any multiagent system. Such interactions are usually regulated by protocols, which are typically defined at design-time. However, in many situations a protocol may not exist or the available protocols may not fit the needs of the agents. In order to deal with such situations agents should be able to generate protocols at runtime. In this paper we develop a three-phase framework to enable agents to create a commitment protocol dynamically. In the first phase one of the agents generates candidate commitment protocols, by considering its goals, its abilities and its knowledge about the other agents’ services. We propose two algorithms that ensure that each generated protocol allows the agent to reach its goals if the protocol is enacted. The second phase is ranking of the generated protocols in terms of their expected utility in order to select the one that best suits the agent. The third phase is the negotiation of the protocol between agents that will enact the protocol so that the agents can agree on a protocol that will be used for enactment. We demonstrate the applicability of our approach using a case study.

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JAAMAS Journal 2010 Journal Article

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

Reyhan Aydoğan
Pınar Yolum

Abstract We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.

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JAAMAS Journal 2008 Journal Article

Evolving service semantics cooperatively: a consumer-driven approach

Murat Şensoy
Pınar Yolum

Abstract Commerce relies on dynamic creation and modification of services. New service offerings or service demands come into play frequently. Whereas traditional commerce supports creation of new service demands from consumers, e-commerce has so far expected service providers to come up with desirable new service offerings and assigned service consumers a passive role in the process. That is, current e-commerce architecture lacks a consumer-driven approach for the generation of new service descriptions. This paper bridges this gap by proposing a multiagent system of consumers that represent their service needs semantically using ontologies. Using our proposed approach, agents can create new service descriptions, share them with interested others, and use service descriptions that are created by other agents. Hence, more accurate concepts describing consumers’ service needs are cooperatively and iteratively created. This leads to a society of consumers with different but overlapping ontologies where mutually accepted service concepts emerge based on consumers’ exchange of service descriptions. Our simulations of consumer societies show that allowing cooperative evolution of service ontologies facilitates better representation of consumers’ service needs. Further, through cooperation, not only more useful service concepts emerge over time, but also ontologies of consumers having similar service needs become aligned gradually.

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EAAI Journal 2007 Journal Article

Experience-based service provider selection in agent-mediated E-Commerce

Murat Şensoy
F. Canan Pembe
Hande Zırtıloğlu
Pınar Yolum
Ayşe Bener

For a given service demand, it is necessary to select a suitable service provider among many possibilities. An accurate selection is difficult when consumers do not have a significant history with many of the service providers and thus need to interact with others to make informed selections. In traditional approaches, consumers rate the service providers and exchange these ratings among each other. Contrary to traditional, rating-based service provider selection, this paper advocates an objective, experience-based approach in which consumers record their experiences with service providers rather than the overall, subjective ratings. A consumer's experience with a provider captures the requested service and the delivered service in terms of service-specific attribute values. When an experience is transferred from one service consumer to another, the receiving consumer evaluates the experience using her own evaluation criteria. By sharing experiences, service consumers can model service providers accurately and thus make better selections for their needs. Rating-based strategies use highly subjective information for decision making since ratings depend on satisfaction criteria of the rater. However, the proposed method uses experiences, which do not include any interpretation. Comparisons of experience-based and rating-based strategies show that the experience-based approach results in higher customer satisfaction rates in many real-life settings.

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