Name
Abstract | ||
|---|---|---|
In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Most of the macro generation techniques aim for using the same set of generated macros on every problem instance of a given domain. This limits the usefulness of macros in scenarios where the environment and thus the structure of instances is dynamic, such as in real-world applications. Moreover, despite the wide availability of parallel processing units, there is a lack of approaches that can take advantage of multiple parallel cores, while exploiting macros. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues by exploiting multiple cores for combining promising macros -taken from a given pool- in different sets, while solving continuous streams of problem instances. Our empirical study, involving 5 state-of-the-art planning engines and a large number of planning instances, demonstrates the effectiveness of the proposed MEvo approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-70169-1_33 | AI*IA 2017 ADVANCES IN ARTIFICIAL INTELLIGENCE |
DocType | Volume | ISSN |
Conference | 10640 | 0302-9743 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mauro Vallati | 1 | 216 | 46.63 |
| Lukás Chrpa | 2 | 99 | 23.15 |
| Ivan Serina | 3 | 825 | 57.97 |