Faruk Polat

Department of Computer Engineering
Scopus Author ID
Web of Science Researcher ID
Incremental multi-agent path finding
Semiz, Fatih; Polat, Faruk (Elsevier BV, 2021-03-01)
Existing multi-agent path finding (MAPF) algorithms are offline methods that aim at finding conflict-ree paths for more than one agent. In many real-life applications it is possible that a multi-agent plan cannot be fully ...
Compact Frequency Memory for Reinforcement Learning with Hidden States.
Polat, Faruk; Aydin, Huseyin; Cilden, Erkin (2019-10-28)
Memory-based reinforcement learning approaches keep track of past experiences of the agent in environments with hidden states. This may require extensive use of memory that limits the practice of these methods in a real-li...
Effective feature reduction for link prediction in location-based social networks
Bayrak, Ahmet Engin; Polat, Faruk (SAGE Publications, 2019-10-01)
In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, com...
Automatic landmark discovery for learning agents under partial observability
DEMİR, ALPER; Cilden, Erkin; Polat, Faruk (Cambridge University Press (CUP), 2019-08-02)
In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Obse...
DEMİR, ALPER; Cilden, Erkin; Polat, Faruk (World Scientific Pub Co Pte Lt, 2019-03-01)
Options framework is one of the prominent models serving as a basis to improve learning speed by means of temporal abstractions. An option is mainly composed of three elements: initiation set, option's local policy and ter...
A context aware model for autonomous agent stochastic planning
Ekmekci, Ömer; Polat, Faruk (Elsevier BV, 2019-02-01)
Markov Decision Processes (MDPs) are not able to make use of domain information effectively due to their representational limitations. The lacking of elements which enable the models be aware of context, leads to unstructu...
An Evolutionary Approach for Detecting Communities in Social Networks
Polat, Faruk; Ozturk, Koray (2019-01-01)
Reducing Features to Improve Link Prediction Performance in Location Based Social Networks, Non-Monotonically Selected Subset from Feature Clusters
Bayrak, Ahmet Engin; Polat, Faruk (2019-01-01)
In most cases, feature sets available for machine learning algorithms require a feature engineering approach to pick the subset for optimal performance. During our link prediction research, we had observed the same challen...
Effective induction of gene regulatory networks using a novel recommendation method
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2019-01-01)
In this paper, we introduce a method based on recommendation systems to predict the structure of Gene Regulatory Networks (GRNs) making use of data from multiple sources. Our method is based on collaborative filtering appr...
Landmark Based Reward Shaping in Reinforcement Learning with Hidden States
Demir, Alper; Cilden, Erkin; Polat, Faruk (2019-01-01)
While most of the work on reward shaping focuses on fully observable problems, there are very few studies that couple reward shaping with partial observability. Moreover, for problems with hidden states, where there is no ...
Citation Formats