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Middle East Technical University


On Inference of Sense of Place from Geo-Social Networks

AAAI Conferences

The aim of the study is to investigate whether individuals report the places they are attached to in location-based services, and whether there is a relationship between the attachment scores of these places and their corresponding check-in frequency information. A survey is conducted to measure the degree of place attachment of individuals based on self-reported locations. Then their Foursquare log data is collected which includes their check-in and venue information. Our results show that the majority of the participants check in to locations that they are attached to. Attachment score is shown to be related to the check-in frequency. The tips left for venues include terms and phrases, suggesting place attachment.


Sirin

AAAI Conferences

The goal of controlling a gene regulatory network (GRN) is to generate an intervention strategy, i.e., a control policy, such that by applying the policy the system will avoid undesirable states. In this work, we propose a method to control GRNs by using Batch Mode Reinforcement Learning (Batch RL). Our idea is based on the fact that time series gene expression data can actually be interpreted as a sequence of experience tuples collected from the environment. Existing studies on this control task try to infer a model using gene expression data and then calculate a control policy over the constructed model. However, we propose a method that can directly use the available gene expression data to obtain an approximated control policy for gene regulation that avoids the time consuming model building phase. Results show that we can obtain policies for gene regulation systems of several thousands of genes just in several seconds while existing solutions get stuck for even tens of genes. Interestingly, the reported results also show that our method produces policies that are almost as good as the ones generated by existing model dependent methods.


Employing Batch Reinforcement Learning to Control Gene Regulation Without Explicitly Constructing Gene Regulatory Networks

AAAI Conferences

The goal of controlling a gene regulatory network (GRN) is to generate an intervention strategy, i.e., a control policy, such that by applying the policy the system will avoid undesirable states. In this work, we propose a method to control GRNs by using Batch Mode Reinforcement Learning (Batch RL). Our idea is based on the fact that time series gene expression data can actually be interpreted as a sequence of experience tuples collected from the environment. Existing studies on this control task try to infer a model using gene expression data and then calculate a control policy over the constructed model. However, we propose a method that can directly use the available gene expression data to obtain an approximated control policy for gene regulation that avoids the time consuming model building phase. Results show that we can obtain policies for gene regulation systems of several thousands of genes just in several seconds while existing solutions get stuck for even tens of genes. Interestingly, the reported results also show that our method produces policies that are almost as good as the ones generated by existing model dependent methods.


Ekmekci

AAAI Conferences

In poker, players tend to play sub-optimally due to theuncertainty in the game. Payoffs can be maximized byexploiting these sub-optimal tendencies. One way of realizingthis is to acquire the opponent strategy by recognizingthe key patterns in its style of play. Existing studieson opponent modeling in poker aim at predicting opponent'sfuture actions or estimating opponent's hand.In this study, we propose a machine learning methodfor acquiring the opponent's behavior for the purpose ofpredicting opponent's future actions.We derived a numberof features to be used in modeling opponent's strategy.Then, an ensemble learning method is proposed forgeneralizing the model. The proposed approach is testedon a set of test scenarios and shown to be effective.


Learning Strategies for Opponent Modeling in Poker

AAAI Conferences

In poker, players tend to play sub-optimally due to theuncertainty in the game. Payoffs can be maximized byexploiting these sub-optimal tendencies. One way of realizingthis is to acquire the opponent strategy by recognizingthe key patterns in its style of play. Existing studieson opponent modeling in poker aim at predicting opponent’sfuture actions or estimating opponent’s hand.In this study, we propose a machine learning methodfor acquiring the opponent’s behavior for the purpose ofpredicting opponent’s future actions.We derived a numberof features to be used in modeling opponent’s strategy.Then, an ensemble learning method is proposed forgeneralizing the model. The proposed approach is testedon a set of test scenarios and shown to be effective.


Düzgün

AAAI Conferences

Reading groups domain is a new domain for group recommenders. In this paper we propose a web based group recommender system which is called BoRGo: Book Recommender for Reading Groups, for reading groups domain. BoRGo uses a new information filtering technique which uses the difference between positive and negative feedbacks about a feature of a user profile and also presents an interface for after recommendation processes like achieving a consensus on the reading list.


A Web-Based Book Recommendation Tool for Reading Groups

AAAI Conferences

Reading groups domain is a new domain for group recommenders. In this paper we propose a web based group recommender system which is called BoRGo: Book Recommender for Reading Groups, for reading groups domain. BoRGo uses a new information filtering technique which uses the difference between positive and negative feedbacks about a feature of a user profile and also presents an interface for after recommendation processes like achieving a consensus on the reading list.


Components of the Shape Revisited

AAAI Conferences

There are multiple and even interacting dimensions along which shape representation schemes may be compared and contrasted. In this paper, we focus on the following ques- tion. Are the building blocks in a compositional model lo- calized in space (e.g. as in part based representations) or are they holistic simplifications (e.g. as in spectral representa- tions)? Existing shape representation schemes prefer one or the other. We propose a new shape representation paradigm that encompasses both choices.


Wikipedia Missing Link Discovery: A Comparative Study

AAAI Conferences

In this paper, we describe our work on discovering missing links in Wikipedia articles. This task is important for both readers and authors of Wikipedia. The readers will benefit from the increased article quality with better navigation support. On the other hand, the system can be employed to support the authors during editing. This study combines the strengths of different approaches previously applied for the task, and adds its own techniques to reach satisfactory results. Because of the subjectivity in the nature of the task; automatic evaluation is hard to apply. Comparing approaches seems to be the best method to evaluate new techniques, and we offer a semi-automatized method for evaluation of the results. The recall is calculated automatically using existing links in Wikipedia. The precision is calculated according to manual evaluations of human assessors. Comparative results for different techniques are presented, showing the success of our improvements. We employ Turkish Wikipedia, we are the first to study on it, to examine whether a small instance is scalable enough for such purposes.