  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.
