Papers
10
Total Citations
127
H-Index
9
About
Eyal Amir is a leading researcher in artificial intelligence, with key contributions spanning knowledge representation, cognitive robotics, and probabilistic reasoning. His work bridges symbolic logic and statistical inference, most notably through the development of lifted relational Kalman filtering, which enables efficient state estimation in large-scale dynamic systems with relational structures—a breakthrough with applications in robotics, finance, and environmental engineering. His highly cited 2013 paper on this topic (26 citations) exemplifies his impact. Amir has also advanced cognitive robotics by using text-based adventure games as a testbed for reasoning under incomplete information, and he has explored the future of human-android interaction. His early work on logic-based subsumption architectures and automatic decomposition of logical theories laid foundational principles for modular knowledge representation. With over 125 total citations across his top papers, Amir’s research continues to influence how intelligent systems reason, plan, and interact in complex, uncertain environments.
Research Focus
Key Achievements
Top Papers
- 1Lifted Relational Kalman Filtering26 citations · 2013
- 2Logic-based subsumption architecture16 citations · 2003
- 3Human—Android Interaction in the Near and Distant Future14 citations · 2009
- 4Adventure Games: A Challenge for Cognitive Robotics12 citations · 2002
- 5
- 6Sampling First Order Logical Particles12 citations · 2012
- 7Lifted Relational Variational Inference11 citations · 2012
- 8Dividing and conquering logic10 citations · 2001
- 9Factor-guided motion planning for a robot arm9 citations · 2007
- 10Research Challenges and Opportunities in Knowledge Representation5 citations · 2013