Meir Goldenberg

Papers

Total Citations

H-Index

About

Meir Goldenberg is a computer scientist whose research focuses on heuristic search and pathfinding, with significant contributions to memory-based heuristics for navigation in complex domains. His most notable work, "The compressed differential heuristic" (2017), introduces the compressed differential heuristic (CDH)—a family of memory-efficient variants of the differential heuristic (DH), a widely used technique for accelerating pathfinding in polynomial spaces. This work addresses a critical challenge in AI: balancing heuristic accuracy with memory constraints, enabling faster and more scalable search algorithms. While his citation impact is still emerging, Goldenberg’s research builds on foundational ideas in heuristic search, offering practical improvements for robotics, video games, and geographic information systems. His work is particularly valuable for students and researchers seeking to understand how compression techniques can enhance heuristic performance without sacrificing optimality. By refining the differential heuristic, Goldenberg has contributed to a deeper understanding of memory-based heuristics, paving the way for more efficient pathfinding in real-world applications.