Name
Abstract | ||
|---|---|---|
General purpose planners enable AI systems to solve many different types of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this paper, we empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language. PDDL is designed for automated planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used for solving planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general purpose planning systems for a particular domain. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Empirical comparison,Business system planning,Software engineering,General purpose,Computer science,Action language,Planner,Artificial intelligence,Strengths and weaknesses,Answer set programming,Planning Domain Definition Language,Machine learning |
DocType | Volume | Citations |
Journal | abs/1804.08229 | 1 |
PageRank | References | Authors |
0.36 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuqian Jiang | 1 | 3 | 3.79 |
| Shiqi Zhang | 2 | 119 | 22.46 |
| Piyush Khandelwal | 3 | 81 | 9.72 |
| Peter Stone | 4 | 6878 | 688.60 |