Babak Esfandiari
Papers
9
Total Citations
91
H-Index
5
About
Babak Esfandiari is a leading researcher in autonomous systems and multi-agent architectures, with a focus on bridging the gap between simulated agents and real-world robotics. His core contributions lie in two complementary areas: case-based reasoning (CBR) for learning by observation, and the deployment of Belief-Desire-Intention (BDI) agents in physical environments. Esfandiari’s work on CBR frameworks allows non-expert users to train robots by simply demonstrating tasks, addressing the critical challenge of programming autonomous systems in complex, partially observable settings. His highly cited 2011 paper on a CBR framework for learning by observation (29 citations) and his 2008 study on spatially-aware CBR in robotic soccer (20 citations) have been foundational for imitation learning in robotics. More recently, Esfandiari has pioneered the practical application of BDI architectures, most notably through the “Agent in a Box” framework and a landmark 2020 project on campus mail delivery using autonomous robots in Carleton University’s tunnel system (14 citations). This work demonstrates how sophisticated agent reasoning can be effectively transferred from simulation to real-world navigation, solidifying Esfandiari’s reputation as a key figure in making intelligent, autonomous mobile robots a practical reality.
Research Focus
Key Achievements
Top Papers
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- 3Toward Campus Mail Delivery Using BDI14 citations · 2020
- 4Learning state-based behaviour using temporally related cases11 citations · 2011
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- 7BDI for Autonomous Mobile Robot Navigation3 citations · 2022
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- 9Software Agents and Situatedness" Being Where?2 citations · 2000