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Reyhan Aydoǧan

Bio: Reyhan Aydoǧan is an academic researcher. The author has contributed to research in topics: Negotiation & Ontology (information science). The author has an hindex of 2, co-authored 3 publications receiving 9 citations.

Papers
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Proceedings ArticleDOI
10 May 2010
TL;DR: This work develops negotiation strategies that work on qualitative preference representations, such as CP-nets that require only partial preference information.
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.

8 citations

Proceedings ArticleDOI
12 May 2008
TL;DR: This research develops an automated negotiation approach in which the consumer takes the preferences of the user in an efficient way and uses these preferences in the generation of request.
Abstract: In e-commerce, for some cases the service requested by the consumer cannot be fulfilled by the producer. In such cases, service consumers and producers need to negotiate their service requirements and offers. Whereas some multiagent negotiation approaches treat the price as the primary construct for negotiation, we consider that the service content is as much important as the price. Therefore, this study mainly focuses on the content of the service described in a common ontology accessed by both agents for common understanding. Acquiring user's preferences and acting upon these preferences are crucial tasks for a consumer agent as far as the negotiation is concerned. Since the size of complete preference information increases exponentially with the number of attributes and size of domain, it is required to keep these preferences in a compact way. There are a variety of ways of representing preferences and using these structures for automatic generation of consumer's request. This research develops an automated negotiation approach in which the consumer takes the preferences of the user in an efficient way and uses these preferences in the generation of request. For this purpose, we design several strategies to generate requests to take the best offer by the producer. On the other side, in order to obtain a more effective negotiation results the producer tries to learn the consumer preferences from the bid exchanges incrementally in order to refine its offer over time. Furthermore, for some complicated services desired by the consumer, a single producer by itself may not meet the consumer's needs. In such cases, the system should allow consumers negotiating with multiple service producers as far as composite services are concerned.

2 citations

Proceedings Article
10 May 2009
TL;DR: This work proposes a negotiation framework where producer agents learn the preferences of consumer preferences over time and negotiates based on this new knowledge, based on inductive learning but also incorporates the idea of revision.
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.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: A lifecycle model of a negotiation agent in which the individual components that comprise automated negotiation and the interactions between those components are identified and the taxonomy of opponent models is presented.
Abstract: Negotiation is a complex process. The decision making involved in several stages of negotiation makes its automation complex. In this paper we present a lifecycle model of a negotiation agent in which we identify the individual components that comprise automated negotiation and the interactions between those components. We present a survey of methods used in the automated negotiation literature fitting them to the components of our lifecycle model. While discussing the opponent modeling component, we present the taxonomy of opponent models. The lifecycle model is generic enough to accommodate most of the frameworks in the literature. To this end we fit the methods used in some of the automated negotiation frameworks in the literature to the lifecycle.

22 citations

Book ChapterDOI
TL;DR: Two variants of feedback based multilateral negotiation protocol in which a mediator agent generates bids and negotiating agents give their feedback about those bids are presented.
Abstract: When more than two participants have a conflict of interest, finding a mutual agreement may entail a time consuming process especially when the number of participants is high. Automated negotiation tools can play a key role in providing effective solutions. This paper presents two variants of feedback based multilateral negotiation protocol in which a mediator agent generates bids and negotiating agents give their feedback about those bids. We investigate different types of feedback given to the mediator. The mediator uses agents’ feedback to models each agent’s preferences and accordingly generates well-targeted bids over time rather than arbitrary bids. Furthermore, the paper investigates the performance of the protocols in an experimental setting. Experimental results show that the proposed protocols result in a reasonably good outcome for all agents in a relatively short time.

20 citations

Journal ArticleDOI
TL;DR: This paper proposes heuristics that enable the use of CP-nets in utility-based negotiations by generating total orderings and shows that they can achieve comparable performance in terms of the outcome utility.
Abstract: CP-nets have proven to be an effective representation for capturing preferences. However, their use in automated negotiation is not straightforward because, typically, preferences in CP-nets are partially ordered and negotiating agents are required to compare any two outcomes based on a request and an offer in order to negotiate effectively. If agents know how to generate total orders from their CP-nets, they can make this comparison. This paper proposes heuristics that enable the use of CP-nets in utility-based negotiations by generating total orderings. To validate this approach, the paper compares the performance of CP-nets with our heuristics with the performance of UCP-nets that are equipped with complete preference orderings. Our results show that we can achieve comparable performance in terms of the outcome utility. More importantly, one of our proposed heuristics can achieve this performance with significantly smaller number of interactions compared to UCP-nets.

20 citations

Journal ArticleDOI
TL;DR: A time-based bidding strategy has been introduced, which uses the opponent model to concede more adaptively to the opponents, thereby achieving an improved utility, social welfare, and fairness for the agent.
Abstract: In automatic negotiation, intelligent agents try to reach the best deal possible on behalf of their owners. In previous studies, opponent modeling of a negotiator agent has been used to tune the final bid out of a group of bids chosen by the agent’s strategy. In this research, a time-based bidding strategy has been introduced, which uses the opponent model to concede more adaptively to the opponents, thereby achieving an improved utility, social welfare, and fairness for the agent. By modeling the preference profile of the opponent during the negotiation session, this strategy sets its concession factor proportional to the model. Experiments show that in comparison to state-of-the-art agents, this agent makes better agreements in terms of individual utility and social welfare in small and medium-sized domains and can, in some cases, increase the performance up to 10%. The proposed agent successfully gets the deal up to 37% closer to best social bids in terms of distance to the Pareto frontier and the Nash point. An implementation based on the proposed strategy was used in an agent called AgreeableAgent, which participated in the international ANAC 2018 and won first place in individual utility rankings.

13 citations