Enhancing Hierarchical Task Network Planning through Ant Colony Optimization in Refinement Process

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 into the refinement phase of HTN planning, the authors aim to leverage its adaptive nature and decentralized decision-making to improve plan generation. This paper involves the development of a hybrid strategy called ACO-HTN, which combines HTN planning with ACO-based plan selection. This technique enables the system to adaptively refine plans by guiding the search towards optimal solutions. To evaluate the effectiveness of the proposed technique, this paper conducts empirical experiments on various domains and benchmark datasets. Our results demonstrate that the ACO-HTN strategy enhances the efficiency and effectiveness of HTN planning, outperforming traditional methods in terms of solution quality and computational performance.