Artificial Intelligence, Machine Learning
AI & Cognitive Robotics
Cognitive Robotics (CogRobo) is concerned with endowing robotic or software agents with higher level cognitive functions that involve reasoning, for example, about goals, perception, actions, the mental states of other agents, collaborative task execution. Our research has mainly been on bridging the gap between high-level reasoning and low-level control, involving both theoretical and hands-on components.
Due the interdisciplinary and synergistic nature of this research area, we study various topics, including kinematic and dynamic modeling of robots, architectures for robot control, world maps and localization, object recognition, manipulation and path planning, human-robot interaction, AI planning, sensing and monitoring, diagnosis, learning, representation and reasoning formalisms and algorithms, and methods for coupling high-level reasoning with low-level feasibility checks. We apply our methods in different robotic application domains, such as robotic manipulation, cognitive factories, service robotics, cognitive rehabilitation robotics, computer games, cloud robotics.
Knowledge Representation and Reasoning
Knowledge Representation and Reasoning (KR&R) is the study of representing knowledge in such a way that a computer can reason about it (infer appropriate knowledge from it) to behave intelligently. We conduct research in various areas in KR&R, such as answer set programming, declarative problem solving, reasoning about actions and change, planning, ontological representation and reasoning, query answering over big data (on the web and on the cloud), explanation generation.
Our research in KR&R has mainly been along two lines: on the mathematical foundations of KR&R, and their applications to computer sciences and other sciences (e.g., robotics, logical puzzles, computer games, VLSI design, historical linguistics, Semantic Web, biomedical informatics, and computational biology).
￼Machine Learning field is concerned with building machines that learns automatically from available data. Our work in the general machine learning and pattern recognition area includes the use of machine learning techniques in various applications and some work in machine learning theory. Current concentration areas are biometrics (online and offline signature verification, fingerprint verification, biometric privacy), plant recognition from photographs, online and offline handwriting recognition and sentiment analysis and information summarization for Turkish and English.