Alberto Reyes
Papers
2
Total Citations
22
H-Index
2
About
Alberto Reyes is a pioneer in the intersection of decision theory, robotics, and artificial intelligence, with a particular focus on solving complex, real-world problems under uncertainty. His key research areas include Markov Decision Processes (MDPs), abstraction and refinement techniques, and socially interactive robotics. Reyes’s most notable contribution is his groundbreaking work on continuous and hybrid MDPs, where he introduced a novel two-phase approach that partitions state space based on reward functions to achieve efficient approximate solutions—a method that has garnered 13 citations and remains foundational for researchers tackling high-dimensional decision problems. In the realm of social robotics, Reyes developed a decision-theoretic framework for task coordination, leveraging multiply sectioned Markov decision processes (MS-MDPs) within a three-layer hybrid control architecture. This work, cited 9 times, enables robots to navigate complex social environments and coordinate tasks effectively, marking a significant step toward more autonomous and interactive machines. Through these contributions, Reyes has advanced both theoretical foundations and practical applications, influencing fields from automated planning to human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Abstraction and Refinement for Solving Continuous Markov Decision Processes.13 citations · 2006
- 2A decision theoretic approach for task coordination in social robots9 citations · 2005