Study on Operational Plan by Case-Based-Reasoning

Operational plan generated by case-based-reasoning (OP-CBR) is based on reusing past successful plans for solving new problems. OP-CBR is particularly useful in environments where the large amount of time required to traverse extensive search spaces makes traditional planning techniques unsuitable. In particular, in real-time domains, past plans need to be retrieved and adapted in real time and efficient plan adaptation techniques are required. We have developed real time adaptation techniques for case based planning and specifically applied them to the OP-CBR system. In our framework, when a plan is retrieved, a plan dependency graph is inferred to capture the relations between actions in the plan suggested by that case. The case is then adapted in real-time using its plan dependency graph. This allows the system to create and adapt plans in an efficient and effective manner while performing the task. Our techniques have been implemented in the OP-CBR system. We analyze our approach and prove that the complexity of the plan adaptation stage is polynomial in the size of the plan. We also provide bounds on the final size of the adapted plan under certain assumptions.