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Efficient Plan Adaptation through Replanning Windows and Heuristic Goals

100%

Gerevini A. E. , Serina I.

Fundamenta Informaticae

2010

tom Vol. 102, nr 3/4

287-323

Fast plan adaptation is important in many AI applications. From a theoretical point of view, in the worst case adapting an existing plan to solve a new problem is no more efficient than a complete regeneration of the plan. However, in practice plan adaptation can be much more efficient than plan generation, especially when the adapted plan can be obtained by performing a limited amount of changes to the original plan. In this paper, we investigate a domain-independent method for plan adaptation that modifies the original plan by replanning within limited temporal windows containing portions of the plan that need to be revised. Each window is associated with a particular replanning subproblem that contains some "heuristic goals" facilitating the plan adaptation, and that can be solved using different planning methods. An experimental analysis shows that, in practice, adapting a given plan for solving a new problem using our techniques can be much more efficient than replanning from scratch.

Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning

80%

Vallati M. , Serina I. , Saetti A. , Gerevini A. E.

Fundamenta Informaticae

2016

tom Vol. 149, nr 1/2

209--240

Case-based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is very effective when similar reuse candidates can be efficiently and effectively chosen. In this paper, we study an innovative technique based on planning problem features for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. A problem feature is a characteristic –usually provided under the form of a number– of the instance that can be automatically derived from the problem specification, domain and search space analyses, or different problem encodings. Given a planning problem to solve, its features are extracted and compared to those of problems stored in the case base, in order to identify most similar problems. Since the use of existing planning features is not always able to effectively distinguish between problems within the same planning domain, we introduce a large number of new features. An experimental analysis in this paper investigates the best set of features to be exploited for retrieving plans in case-based planning, and shows that our feature-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system.