Case-Based Merging Techniques in OAKPLAN
This work addresses efficiency challenges in planning systems for AI applications, but it is incremental as it builds on existing case-based methods without a major breakthrough.
The paper tackles the problem of improving planning efficiency by reusing stored plans, showing that case-based planning can be an effective alternative to plan generation when similar reuse candidates are available, though it does not achieve provable efficiency gains.
Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although comparative worst-case complexity analyses of plan generation and reuse techniques reveal that it is not possible to achieve provable efficiency gain of reuse over generation, we show that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen.