Papers
7
Total Citations
359
H-Index
7
About
Nihat Ay is a prominent researcher at the intersection of information theory, autonomous robotics, and complex systems, whose work has significantly advanced our understanding of self-organization and emergent behavior in artificial agents. His most influential contribution lies in applying predictive information — a measure rooted in information theory — as both a metric for behavioral complexity and an objective function guiding autonomous robot development. His 2008 paper on predictive information and explorative behavior, garnering 170 citations, established a foundational framework that has shaped subsequent research in the field. Ay's work consistently demonstrates that information-theoretic principles offer domain-invariant tools for driving autonomous systems, enabling robots to develop sophisticated, coordinated behaviors with minimal external control. His research on information-driven self-organization, explored across multiple publications between 2008 and 2013, shows how maximizing predictive information in the sensorimotor loop naturally produces emergent cooperation and higher-order coordination. He has also contributed to embodied artificial intelligence through investigations of morphological computation, examining how physical body properties offload cognitive demands from central controllers. Collectively, his publications reflect a unifying vision: that fundamental principles of information and complexity can elegantly explain and engineer intelligent, adaptive behavior in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Predictive information and explorative behavior of autonomous robots170 citations · 2008
- 2Information Driven Self-Organization of Complex Robotic Behaviors78 citations · 2013
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- 6On the Cross-Disciplinary Nature of Guided Self-Organisation13 citations · 2013
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