Planning has achieved significant progress in recent years. Among the various approaches to scale up plan synthesis, the use of macro-actions has been widely explored. As a first stage towards the development of a solution to learn on-line macro-actions, we propose an algorithm to identify useful macro-actions based on data mining techniques. The integration in the planning search of these learned macro-actions shows significant improvements over six classical planning benchmarks.
By contrast, an on-line approach remove the need of extra training problems and off-line filtering. Coles and Smith described Marvin planner in @cite_4 . It identifies regions in the search space where the heuristic values of all successors is greater than or equal to the best seen so far. Then, it learns the escaping macro-actions to use them in similar regions during the search. This work was improved in @cite_12 by keeping libraries of macro-actions for use on future problems. @cite_6 extended their early technique by generating useful macros from outer entanglements in the search without an offline learning phase.
@cite_4 @cite_6 @cite_12
2157466031 368544695
This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates. Macro-operator ("macro", for short) generation is a well-known technique that is used to speed-up the planning process. Most published work on using macros in automated planning relies on an offline learning phase where training plans, that is, solutions of simple problems, are used to generate the macros. However, there might not always be a place to accommodate training. In this paper we propose OMA, an efficient method for generating useful macros without an offline learning phase, by utilising lessons learnt from existing macro learning techniques. Empirical evaluation with IPC benchmarks demonstrates performance improvement in a range of state-of-the-art planning engines, and provides insights into what macros can be generated without training.