基于检索失败分析的存储和索引方案推导
Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures
摘要：
基于案例的规划（CBP）提供了一种方法来扩展领域独立的规划，以解决复杂领域中的大型问题。它取代了详细和冗长的搜索与检索和调整以前的规划经验的解决方案。一般来说，CBP已被证明可以提高生成（从头开始）规划的性能。然而，它提供的性能改进依赖于对问题相似性的充分判断。特别是，虽然CBP可以大大减少规划工作的整体，它是一个错误的检索问题。CBP的成功取决于这些检索错误相对较少。本文描述了基于实例的计划器DERSNLP+EBL的重放框架的设计与实现。DERSNLP+EBL通过结合基于解释的学习技术扩展了当前的CBP方法，使其能够解释并从遇到的检索失败中学习。这些技术用于在做出错误决定时根据反馈改进对案例相似性的判断。采用相同的故障分析方法，通过添加维修实例建立实例库。大型问题被拆分并存储为单个目标子问题。多目标问题仅在这些较小的案例无法合并为完整解决方案时才被存储。对这种方法的实证评估表明，从有经验的检索失败中学习的优势。
英文摘要：
Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
