Aris Valtazanos
Papers
6
Total Citations
26
H-Index
3
About
Aris Valtazanos is a researcher in multi-robot systems and human-robot interaction, with a focus on strategic decision-making under uncertainty. His work addresses the fundamental challenge of enabling autonomous agents to infer the intentions of adversaries and influence their beliefs in mixed robotic domains—environments where teleoperated and autonomous robots coexist. Valtazanos’s most cited paper, “Intent inference and strategic escape in multi-robot games with physical limitations and uncertainty” (7 citations), introduces a framework for robots to estimate opponent strategies while accounting for sensory noise and physical constraints. In “Bayesian interaction shaping” (7 citations), he develops a learning approach that allows robots to strategically influence the beliefs of other agents, including humans, moving beyond simple instruction-following to proactive, intelligent behavior. His work on limited perception (“Evaluating the effects of limited perception on interactive decisions,” 5 citations) explores how degraded sensory information impacts decision-making in human-robot teams. Valtazanos’s contributions are notable for bridging game theory, Bayesian inference, and robotics, offering practical solutions for real-world applications like search-and-rescue or autonomous driving, where agents must collaborate or compete with incomplete information.
Research Focus
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Top Papers
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