Sequential planning portfolios exploit the complementary strengths of different planners. Similarly, automated algorithm configuration tools can customize parameterized planning …
M Vallati, L Chrpa, D Kitchin - AI Communications, 2015 - content.iospress.com
In recent years the field of Automated Planning has significantly advanced and several powerful domain-independent planners have been developed. However, none of these …
A Torralba, S Edelkamp, P Kissmann - Proceedings of the International …, 2013 - ojs.aaai.org
Symbolic search with binary decision diagrams (BDDs) often saves huge amounts of memory and computation time. In this paper we propose two general techniques based on …
When it comes to learning control knowledge for planning, most works focus on “how to do it” knowledge which is then used to make decisions regarding which actions should be …
S Nunez, D Borrajo, CL López - Artificial Intelligence, 2015 - Elsevier
In recent years the notion of portfolio has been revived with the aim of improving the performance of modern solvers. For example, Fast Downward Stone Soup and SATzilla …
In this paper, we present FLAP, a partial-order planner that accurately applies the least- commitment principle that governs traditional partial-order planning. FLAP fully exploits the …
In the field of automated planning, the central research focus is on domain‐independent planning engines that accept planning tasks (domain models and problem descriptions) in a …
Domain independent planning engines accept a planning task description in a language such as PDDL and return a solution plan. Performance of planning engines can be …
Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably …