Alfonso Gerevini

Papers

Total Citations

H-Index

About

Alfonso Gerevini is a researcher whose work sits at the intersection of artificial intelligence, autonomous robotics, and symbolic planning. His investigations focus on enabling robotic agents to operate intelligently in dynamic, previously unknown environments — a challenge that sits at the frontier of modern AI research. Gerevini's most notable contribution centers on the online grounding of symbolic planning domains, a technically demanding problem that arises when a robot must translate abstract, high-level planning representations into actionable, environment-specific knowledge without prior familiarity with its surroundings. His work addresses how agents can simultaneously explore unknown environments and ground symbolic domains in real time, bridging the gap between classical AI planning theory and practical robotic deployment. This research has accumulated citations across both its 2021 and 2022 iterations, reflecting growing recognition within the robotics and AI planning communities. The repeated publication and citation of this work signals its relevance to researchers tackling autonomous exploration, task planning, and grounded reasoning in unstructured environments. For students and researchers working on embodied AI, robot autonomy, or knowledge representation, Gerevini's contributions offer valuable frameworks for thinking about how intelligent agents can adaptively construct the world models they need to plan and act effectively.