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.
|Number of pages||32|
|Publication status||Published - 24 Dec 2016|
|Event||22nd RCRA International Workshop on "Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion" - Ferrara, Italy|
Duration: 22 Sep 2015 → 22 Sep 2015