Goal-Directed Flexible Graphplan

The Graphplan planner has enjoyed considerable success as a planning algorithm for classical planning problems. However, it is argued that this framework is too rigid to capture the full subtlety of many real problems. Hence, a new flexible planning problem is defined and flexible planning method emerges as the times require. This paper just makes research on flexible Graphplan and applies a novel intelligent planning algorithm which expands the flexible planning graph backwards from the goal set and searches a valid plan forwards. In contrast to the existing methods, the algorithm proposes a new approach of backward flexible mutex inference, avoids a complicated process of satisfaction degree propagation, and grantees to acquire the optimal plan. Our method takes into account user’s requirement and taste, strives to improve comprehensive quality of a plan, makes the solution plan more suitable for the needs, and handles the planning problems much closer to the real world. Therefore, our method has its advantage over previous ones in application.