Pinar Yolum

Bogazici University, İstanbul, Istanbul, Turkey

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Publications (70)15.79 Total impact

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    ABSTRACT: Increasingly, software engineering involves open systems consisting of autonomous and heterogeneous participants or agents who carry out loosely coupled interactions. Accordingly, understanding and specifying communications among agents is a key concern. A focus on ways to formalize meaning distinguishes agent communication from traditional distributed computing: meaning provides a basis for flexible interactions and compliance checking. Over the years, a number of approaches have emerged with some essential and some irrelevant distinctions drawn among them. As agent abstractions gain increasing traction in the software engineering of open systems, it is important to resolve the irrelevant and highlight the essential distinctions, so that future research can be focused in the most productive directions. This article is an outcome of extensive discussions among agent communication researchers, aimed at taking stock of the field and at developing, criticizing, and refining their positions on specific approaches and future challenges. This article serves some important purposes, including identifying (1) points of broad consensus; (2) points where substantive differences remain; and (3) interesting directions of future work.
    ACM Transactions on Intelligent Systems and Technology 03/2013; 4(2). DOI:10.1145/2438653.2438655 · 9.39 Impact Factor
  • Ozgur Kafali, Akin Gunay, Pinar Yolum
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    ABSTRACT: As online social networks are becoming part of both social and work life, preserving privacy of their users is becoming tremendously difficult. While these social networks are promising privacy through privacy agreements, everyday new privacy leakages are emerging. Ideally, online social networks should be able to manage and maintain their agreements through well-founded methods. However, the dynamic nature of the online social networks is making it difficult to keep private information contained. We have developed PROTOSS, a run time tool for detecting privacy leakages in online social networks. PROTOSS captures relations among users, their privacy agreements with an online social network operator, and domain-based inference rules. It then uses model checking to detect if an online social network will leak private information.
    Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on; 01/2012
  • Reyhan Aydogan, Pinar Yolum
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    ABSTRACT: Users' preferences play a key role in automated negotiation since they dictate how an agent will act on behalf of its user. However, elicitation of these preferences from the user is difficul when there are dependencies between preferences. In many settings, expecting a user to provide a total ordering of her preferences is unrealistic. Thus, it is essential to build agents that can negotiate with only partial preference information. In order to achieve this goal, we develop negotiation strategies that work on qualitative preference representations, such as CP-nets that require only partial preference information.
    9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, 2010, Volume 1-3; 05/2010
  • Akin Günay, Pinar Yolum
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    ABSTRACT: The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer-oriented and context-dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating-based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars.
    Computational Intelligence 10/2009; 25(4):335 - 366. DOI:10.1111/j.1467-8640.2009.00348.x · 0.87 Impact Factor
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    Murat Şensoy, Pinar Yolum
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    ABSTRACT: In many multiagent approaches, it is usual to assume the existence of a common ontology among agents. However, in dynamic systems, the existence of such an ontology is unrealistic and its maintenance is cumbersome. Burden of maintaining a common ontology can be alleviated by enabling agents to evolve their ontologies personally. However, with different ontologies, agents are likely to run into communication problems since their vocabularies are different from each other. Therefore, to achieve personalized ontologies, agents must have a means to understand the concepts used by others. Consequently, this paper proposes an approach that enables agents to teach each other concepts from their ontologies using examples. Unlike other concept learning approaches, our approach enables the learner to elicit most informative examples interactively from the teacher. Hence, the learner participates to the learning process actively. We empirically compare the proposed approach with the previous concept learning approaches. Our experiments show that using the proposed approach, agents can learn new concepts successfully and with fewer examples.
    07/2009: pages 170-182;
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    Reyhan Aydogan, Pinar Yolum
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    ABSTRACT: Successful negotiation depends on understanding and responding to participants' needs. Many negotiation approaches assume identical needs (e.g., minimizing costs) and do not take into account other preferences of the participants. However, preferences play a crucial role in the outcome of negotiations. Accordingly, we propose a negotiation framework where producer agents learn the preferences of consumer preferences over time and negotiates based on this new knowledge. Our proposed approach 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 customer's preferences. This enables us to learn conjunctive as well as disjunctive preferences. Even if the consumer's preferences are specified in complex ways, such as conditional rules, our approach can learn and guide the producer to create well-targeted offers. Our experimental work shows that our proposed approach completes negotiation faster than similar approaches, especially if the producer will not be able to satisfy consumer's requests properly.
    8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009), Budapest, Hungary, May 10-15, 2009, Volume 2; 05/2009
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    Engineering Societies in the Agents World X, 10th International Workshop, ESAW 2009, Utrecht, The Netherlands, November 18-20, 2009. Proceedings; 01/2009
  • Murat Sensoy, Pinar Yolum
    Agents and Data Mining Interaction, 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15, 2009, Revised Selected Papers; 01/2009
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    Özgür Kafali, Pinar Yolum
    Proceedings of the Second Multi-Agent Logics, Languages, and Organisations Federated Workshops, Turin, Italy, September 7-10, 2009; 01/2009
  • Reyhan Aydogan, Pinar Yolum
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    ABSTRACT: Successful negotiation depends on understanding and responding to participants' needs. Many negotiation approaches assume identical needs and do not take into account other preferences of the participants. However, preferences play a crucial role in the outcome of negotiations. Accordingly, we propose a learning algorithm that can be used by a producer during negotiation to understand consumer's needs and to offer services that respect these 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 customer'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, such as conditional rules, our algorithm can learn and guide the producer to create well-targeted offers.
    Proceedings of the 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2009, Milan, Italy, 15-18 September 2009; 01/2009
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    Özgür Kafali, Pinar Yolum
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    ABSTRACT: In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about whom to interact with can be done soundly. Traditional trust models are based on modeling specific properties of agents, such as their expertise or reliability. Building th ose models requires too many prior interactions to be accurate. This paper proposes an approach that is based on keeping track of outcomes of agent's actions towards others rather t han modeling other agents' performances explicitly. Contrary to existing modeling approaches that require domain knowledge to build models, our proposed approach can be effectively real- ized in multiagent systems when the agent's actions are clea rly identified. Comparisons with other modeling approaches in various environments reveal that our proposed approach can create more precise models in short time and can adjust its behavior quickly when other agents' behaviors change. Keywords-trust; reinforcement learning; modeling;
    2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009, Milan, Italy, 15-18 September 2009, Main Conference Proceedings; 01/2009
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    ABSTRACT: Traditional approaches for solving global optimization problems gen- erally rely on a single algorithm. The algorithm may be hybrid or applied in parallel. Contrary to traditional approaches, this paper p roposes to form teams of algorithms to tackle global optimization problems. Each algorithm is embod- ied and ran by a software agent. Agents exist in a multiagent system and com- municate over our proposed MultiAgent ENvironment for Global Optimization (MANGO). Through communication and cooperation, the agents complement each other in tasks that they cannot do on their own. This paper gives a formal description of MANGO and outlines design principles for developing agents to execute on MANGO. Our case study shows the effectiveness of multiagent teams in solving global optimization problems.
    Agent and Multi-Agent Systems: Technologies and Applications, Third KES International Symposium, KES-AMSTA 2009, Uppsala, Sweden, June 3-5, 2009. Proceedings; 01/2009
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    Murat Sensoy, Pinar Yolum
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    ABSTRACT: Communication among agents requires a common vocabulary to facilitate successful information exchange. One way to achieve this is to assume the existence of a common ontology among communi- cating agents. However, this is a strong assumption, because agents may experience situations that result in independent evolution of their ontologies. When this is the case, agents need to form com- mon grounds to enable communication. Accordingly, this paper proposes an approach in which agents can add new service con- cepts into their service ontologies and teach others services from their ontologies by exchanging service descriptions. This leads to a society of agents with different but overlapping ontologies where mutually accepted services emerge based on agents' exchange of service descriptions. Our simulations of societies show that allow- ing cooperative evolution of local service ontologies facilitates bet- ter representation of agents' needs. Further, through cooperation, not only more useful services emerge over time, but also ontologies of agents having similar service needs become aligned gradually.
    7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 2; 05/2008
  • Murat Sensoy, Pinar Yolum
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper proposes an approach that enables agents to teach each other concepts from their ontologies using examples. Unlike other concept learning approaches, our approach enables the learner to elicit the most informative examples interactively from the teacher. Hence, the learner participates to the learning process actively. We empirically compare the proposed approach with the previous concept learning approaches. Our experiments show that using the proposed approach, agents can learn new concepts successfully and with fewer examples.
    ECAI 2008 - 18th European Conference on Artificial Intelligence, Patras, Greece, July 21-25, 2008, Proceedings; 01/2008
  • Hande Zirtiloglu, Pinar Yolum
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    ABSTRACT: Complaints management is an important application of E-government. Collecting, combining, and ranking citizens' complaints is necessary for effective management. Existing complaints management applications expect the government officials to process each complaint one by one to decide which complaint should be dealt with first. This is time consuming and thus ineffective. To cope with this, we are developing an ontology-based complaints management system to manage complaints. We have developed a complaints ontology with which the complaints of the citizens can be expressed. Further, by specifying constraints on the complaints, the officials can specify which type of complaints are more important than others. We then apply these constraints on the citizens' complaints using a reasoner. This allows us to prioritize the complaints automatically and rank them based on importance. The government officials can then process the ranked list, knowing that they are dealing with the most urgent complaint at any given time.
    Proceedings of the First International Workshop on Ontology-supported Business Intelligence, OBI 2008, Karlsruhe, Germany, October 27, 2008; 01/2008
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    Özgür Kafali, Pinar Yolum
    Trust in Agent Societies, 11th International Workshop, TRUST 2008, Estoril, Portugal, May 12-13, 2008. Revised Selected and Invited Papers; 01/2008
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    Murat Sensoy, Pinar Yolum
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    ABSTRACT: Experience-based service selection refers to selection of service providers using others’ experiences. An agent can represent its experience (its demand and received service) rigorously using an ontology. When agents report their experiences truthfully, experience-based service selection outperforms classical rating-based service selection, in which agents only report ratings for service providers. However, in many setting agents may prefer to lie about their experiences. This paper tackles the problem of handling deceptive information in the context of experience-based service selection. We apply three current approaches for filtering unfair ratings to filtering deceptive experience. We analyze these approaches when multiagent systems have different types of liars and report their performance in filtering deceptive experiences.
    Trust in Agent Societies, 11th International Workshop, TRUST 2008, Estoril, Portugal, May 12-13, 2008. Revised Selected and Invited Papers; 01/2008
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    Akin Günay, Pinar Yolum
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    ABSTRACT: Service matchmaking is the process of finding suitable ser- vices given by the providers for the service requests of con- sumers. Previous approaches to service matchmaking is mostly based on matching the input-output parameters of service requests and service provisions. However, such ap- proaches do not capture the semantics of the services and hence cannot match requests to services eectively. This paper proposes an agent-based approach for matchmaking that is based on capturing the semantics of services and requests formally through temporal logic. Requests are rep- resented as a set of properties and compared to the ser- vice representations using model checking, yielding results on whether a service can satisfy a request or not. By help of domain ontologies, our approach also supports flexible matching, where partially matching services are identified. We provide a general framework, where our approach can work with other existing matchmaking approaches and is integrated with current eorts such as OWL-S and SWRL.
    7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 1; 01/2008
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    Pinar Yolum
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    ABSTRACT: Interaction protocols enable agents to communicate with each other effectively. Whereas several approaches exist to specify interaction protocols, none of them has design tools that can help protocol designers catch semantic protocol errors at design time. As research in networking protocols has shown, flawed specifications of protocols can have disastrous con- sequences. Hence, it is crucial to systematically analyze protocols in time to ensure effective specification. This paper first studies and formalizes important generic properties of commitment protocols that can ease their effective and consistent development significantly. Next, we identify robustness properties of protocols that are useful in determining the applica- bility of protocols in different settings. Since these properties are formal, they can easily be incorporated in a software tool to (semi-)automate the design and specification of commitment protocols. Where appropriate we provide algorithms that can directly be used to check these properties in such a design tool. � 2007 Elsevier B.V. All rights reserved.
    Data & Knowledge Engineering 10/2007; 63(1):137-154. DOI:10.1016/j.datak.2006.12.001 · 1.49 Impact Factor

Publication Stats

750 Citations
15.79 Total Impact Points

Institutions

  • 2005–2013
    • Bogazici University
      • Department of Computer Engineering
      İstanbul, Istanbul, Turkey
  • 2006–2009
    • Istanbul University
      İstanbul, Istanbul, Turkey
  • 2004
    • VU University Amsterdam
      • Department of Artificial Intelligence
      Amsterdam, North Holland, Netherlands
  • 2002–2003
    • North Carolina State University
      • Department of Computer Science
      Raleigh, North Carolina, United States