Add to ORKGNational audiencePlanning 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 four classical planning benchmarks
This paper describes Marvin, a planner that competed in the Fourth International Planning Competitio...
In this work we propose an on-line learning method for learning action rules for planning. The syste...
Despite recent progress in planning, many complex domains and even simple domains with large problem...
International audiencePlanning has achieved significant progress in recent years. Among the various ...
Abstract—Automated planning has achieved significant breakthroughs in recent years. Nonetheless, att...
Intuitively, a system capable of exploiting its past experiences should be able to achieve better pe...
There are many approaches for solving planning problems. Many of these approaches are based on ‘brut...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Many complex domains and even larger problems in simple domains remain challenging in spite of the r...
This paper explores issues encountered when performing on-line management of large collections of ma...
Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In ...
Many fully automated planning systems use a single, domain independent heuristic to guide search and...
Domain re-engineering through macro-actions (i.e. macros) provides one potential avenue for research...
We build a comprehensive macro-learning system and contribute in three different dimensions that hav...
This paper describes Marvin, a planner that competed in the Fourth International Planning Competitio...
In this work we propose an on-line learning method for learning action rules for planning. The syste...
Despite recent progress in planning, many complex domains and even simple domains with large problem...
International audiencePlanning has achieved significant progress in recent years. Among the various ...
Abstract—Automated planning has achieved significant breakthroughs in recent years. Nonetheless, att...
Intuitively, a system capable of exploiting its past experiences should be able to achieve better pe...
There are many approaches for solving planning problems. Many of these approaches are based on ‘brut...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Despite recent progress in planning, many complex domains and even larger problems in simple domains...
Many complex domains and even larger problems in simple domains remain challenging in spite of the r...
This paper explores issues encountered when performing on-line management of large collections of ma...
Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In ...
Many fully automated planning systems use a single, domain independent heuristic to guide search and...
Domain re-engineering through macro-actions (i.e. macros) provides one potential avenue for research...
We build a comprehensive macro-learning system and contribute in three different dimensions that hav...
This paper describes Marvin, a planner that competed in the Fourth International Planning Competitio...
In this work we propose an on-line learning method for learning action rules for planning. The syste...
Despite recent progress in planning, many complex domains and even simple domains with large problem...