REVIEW OF ARTIFICIAL INTELLIGENCE BASED CONCEPT OF AUTOMATED PLANNING

Along with toy obstacles, planners are now able to tackle real-world problems thanks to new developments in computational planning. However, applying computerized planners to real-world problems is far from simple. On the one hand, there is a challenge in precisely defining action models for planning.. Conversely, off-the-shelf planners fall short in many sectors in terms of scaling up and producing good solutions. Planners can use domain-specific control knowledge in these troublesome domains to increase the pace and quality of their solutions. On the other hand, control knowledge definition by hand is highly challenging. This research examines state-of-the-art machine learning methods for automatically defining planning knowledge. It is arranged in accordance with the learning process's objective: automated planning definition