Mohamed Elkawkagy1, Ibrahim A. Elgendy2,*, Ammar Muthanna3,4, Reem Ibrahim Alkanhel5, Heba Elbeh1
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 393-415, 2025, DOI:10.32604/cmc.2025.063766
- 09 June 2025
Abstract Hierarchical Task Network (HTN) planning is a powerful technique in artificial intelligence for handling complex problems by decomposing them into hierarchical task structures. However, achieving optimal solutions in HTN planning remains a challenge, especially in scenarios where traditional search algorithms struggle to navigate the vast solution space efficiently. This research proposes a novel technique to enhance HTN planning by integrating the Ant Colony Optimization (ACO) algorithm into the refinement process. The Ant System algorithm, inspired by the foraging behavior of ants, is well-suited for addressing optimization problems by efficiently exploring solution spaces. By incorporating ACO… More >