Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures

TL;DR

This paper introduces a replay framework for a case-based planner that uses explanation-based learning to analyze and learn from retrieval failures, improving problem similarity judgments and planning efficiency.

Contribution

It extends case-based planning with explanation-based learning to analyze and repair retrieval failures, enhancing planning accuracy and case library quality.

Findings

Learning from retrieval failures improves planning performance.

Explanation-based techniques refine case similarity judgments.

Case library quality is enhanced through failure analysis and repair.

Abstract

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.